1
|
Zhou X, Chen Y, Gui W, Heidari AA, Cai Z, Wang M, Chen H, Li C. Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis. Artif Intell Med 2024; 153:102886. [PMID: 38749310 DOI: 10.1016/j.artmed.2024.102886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 03/17/2024] [Accepted: 04/27/2024] [Indexed: 06/11/2024]
Abstract
Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.
Collapse
Affiliation(s)
- Xinsen Zhou
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Yi Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Wenyong Gui
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Mingjing Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Chengye Li
- Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| |
Collapse
|
2
|
Azzam SM, Emam OE, Abolaban AS. An improved Differential evolution with Sailfish optimizer (DESFO) for handling feature selection problem. Sci Rep 2024; 14:13517. [PMID: 38866847 PMCID: PMC11169489 DOI: 10.1038/s41598-024-63328-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024] Open
Abstract
As a preprocessing for machine learning and data mining, Feature Selection plays an important role. Feature selection aims to streamline high-dimensional data by eliminating irrelevant and redundant features, which reduces the potential curse of dimensionality of a given large dataset. When working with datasets containing many features, algorithms that aim to identify the most valuable features to improve dataset accuracy may encounter difficulties because of local optima. Many studies have been conducted to solve this problem. One of the solutions is to use meta-heuristic techniques. This paper presents a combination of the Differential evolution and the sailfish optimizer algorithms (DESFO) to tackle the feature selection problem. To assess the effectiveness of the proposed algorithm, a comparison between Differential Evolution, sailfish optimizer, and nine other modern algorithms, including different optimization algorithms, is presented. The evaluation used Random forest and key nearest neighbors as quality measures. The experimental results show that the proposed algorithm is a superior algorithm compared to others. It significantly impacts high classification accuracy, achieving 85.7% with the Random Forest classifier and 100% with the Key Nearest Neighbors classifier across 14 multi-scale benchmarks. According to fitness values, it gained 71% with the Random forest and 85.7% with the Key Nearest Neighbors classifiers.
Collapse
Affiliation(s)
- Safaa M Azzam
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, P.O. Box 11795, Helwan, Egypt
| | - O E Emam
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, P.O. Box 11795, Helwan, Egypt
| | - Ahmed Sabry Abolaban
- Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, P.O. Box 11795, Helwan, Egypt.
| |
Collapse
|
3
|
Leiva D, Ramos-Tapia B, Crawford B, Soto R, Cisternas-Caneo F. A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm. Biomimetics (Basel) 2024; 9:283. [PMID: 38786493 PMCID: PMC11117713 DOI: 10.3390/biomimetics9050283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024] Open
Abstract
The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional integer programming solutions may become inefficient in real-life instances. Given this complexity, various metaheuristics have been successfully applied to solve the set-covering problem and related issues. This study introduces, implements, and analyzes a novel metaheuristic inspired by the well-established Growth Optimizer algorithm. Drawing insights from human behavioral patterns, this approach has shown promise in optimizing complex problems in continuous domains, where experimental results demonstrate the effectiveness and competitiveness of the metaheuristic compared to other strategies. The Growth Optimizer algorithm is modified and adapted to the realm of binary optimization for solving the set-covering problem, resulting in the creation of the Binary Growth Optimizer algorithm. Upon the implementation and analysis of its outcomes, the findings illustrate its capability to achieve competitive and efficient solutions in terms of resolution time and result quality.
Collapse
Affiliation(s)
| | | | - Broderick Crawford
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (D.L.); (B.R.-T.); (R.S.); (F.C.-C.)
| | | | | |
Collapse
|
4
|
Chen X, Zhao D, Ji H, Chen Y, Li Y, Zuo Z. Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study. Comput Biol Med 2024; 174:108439. [PMID: 38643596 DOI: 10.1016/j.compbiomed.2024.108439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/26/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024]
Abstract
Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting with cholestasis poses a formidable challenge, given the similarity in their clinical manifestations. To this end, our study endeavors to construct a screening model aimed at prognosticating outcomes in cases of BA. Within this study, we introduce a wrapper feature selection model denoted as bWFMVO-SVM-FS, which amalgamates the water flow-based multi-verse optimizer (WFMVO) and support vector machine (SVM) technology. Initially, WFMVO is benchmarked against eleven state-of-the-art algorithms, with its efficiency in searching for optimized feature subsets within the model validated on IEEE CEC 2017 and IEEE CEC 2022 benchmark functions. Subsequently, the developed bWFMVO-SVM-FS model is employed to analyze a cohort of 870 consecutively registered cases of neonates and infants with cholestasis (diagnosed as either BA or non-BA) from Xinhua Hospital and Shanghai Children's Hospital, both affiliated with Shanghai Jiao Tong University. The results underscore the remarkable predictive capacity of the model, achieving an accuracy of 92.639 % and specificity of 88.865 %. Gamma-glutamyl transferase, triangular cord sign, weight, abnormal gallbladder, and stool color emerge as highly correlated with early symptoms in BA infants. Furthermore, leveraging these five significant features enhances the interpretability of the machine learning model's performance outcomes for medical professionals, thereby facilitating more effective clinical decision-making.
Collapse
Affiliation(s)
- Xuting Chen
- Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Dongying Zhao
- Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Haochen Ji
- The Seventh Research Division, Beihang University (BUAA), Beijing, China
| | - Yihuan Chen
- Department of Neonatology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yahui Li
- Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Zongyu Zuo
- The Seventh Research Division, Beihang University (BUAA), Beijing, China.
| |
Collapse
|
5
|
Qiu F, Heidari AA, Chen Y, Chen H, Liang G. Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data. Sci Rep 2024; 14:8599. [PMID: 38615048 PMCID: PMC11016116 DOI: 10.1038/s41598-024-59064-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 04/06/2024] [Indexed: 04/15/2024] Open
Abstract
Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.
Collapse
Affiliation(s)
- Feng Qiu
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yi Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China.
| | - Guoxi Liang
- Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.
| |
Collapse
|
6
|
Li M, Luo Q, Zhou Y. BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection. Biomimetics (Basel) 2024; 9:187. [PMID: 38534872 DOI: 10.3390/biomimetics9030187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
Collapse
Affiliation(s)
- Mengjun Li
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| |
Collapse
|
7
|
Vega E, Lemus-Romani J, Soto R, Crawford B, Löffler C, Peña J, Talbi EG. Autonomous Parameter Balance in Population-Based Approaches: A Self-Adaptive Learning-Based Strategy. Biomimetics (Basel) 2024; 9:82. [PMID: 38392128 PMCID: PMC10886900 DOI: 10.3390/biomimetics9020082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024] Open
Abstract
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process.
Collapse
Affiliation(s)
- Emanuel Vega
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Valparaíso 2362807, Chile
| | - José Lemus-Romani
- Escuela de Construcción Civil, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile
| | - Ricardo Soto
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Valparaíso 2362807, Chile
| | - Broderick Crawford
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Valparaíso 2362807, Chile
| | - Christoffer Löffler
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Valparaíso 2362807, Chile
| | - Javier Peña
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso, Valparaíso 2362807, Chile
| | - El-Gazhali Talbi
- CNRS/CRIStAL, University of Lille, 59655 Villeneuve d'Ascq, France
| |
Collapse
|
8
|
Thirugnanasambandam K, Murugan J, Ramalingam R, Rashid M, Raghav RS, Kim TH, Sampedro GA, Abisado M. Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance. PeerJ Comput Sci 2024; 10:e1816. [PMID: 38435570 PMCID: PMC10909206 DOI: 10.7717/peerj-cs.1816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/19/2023] [Indexed: 03/05/2024]
Abstract
Background Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal. Methods In this work, a novel optimization algorithm inspired by cuckoo birds' behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model's classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features. Results The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.
Collapse
Affiliation(s)
- Kalaipriyan Thirugnanasambandam
- Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
| | - Jayalakshmi Murugan
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India
| | - Rajakumar Ramalingam
- Centre for Automation, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
| | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India
| | - R. S. Raghav
- School of Computing, SASTRA Deemed University, Villupuram, India
| | - Tai-hoon Kim
- School of Electrical and Computer Engineering, Chonnam National University, Daehak-7, Republic of Korea
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños, Philippines
- Center for Computational Imaging and Visual Innovations, De La Salle University, Malate, Philippines
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila, Philippines
| |
Collapse
|
9
|
Peng L, Cai Z, Heidari AA, Zhang L, Chen H. Hierarchical Harris hawks optimizer for feature selection. J Adv Res 2023; 53:261-278. [PMID: 36690206 PMCID: PMC10658428 DOI: 10.1016/j.jare.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/12/2022] [Accepted: 01/14/2023] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection. OBJECTIVES To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features. METHODS We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets. RESULTS Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms. CONCLUSION Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.
Collapse
Affiliation(s)
- Lemin Peng
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Zhennao Cai
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou 510006, China; College of Information Engineering, Yangzhou University, Yangzhou 225127, China; Research and Development Center for E-Learning , Ministry of Education, Beijing 100039, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| |
Collapse
|
10
|
Yu H, Zhao Z, Heidari AA, Ma L, Hamdi M, Mansour RF, Chen H. An accelerated sine mapping whale optimizer for feature selection. iScience 2023; 26:107896. [PMID: 37860760 PMCID: PMC10582515 DOI: 10.1016/j.isci.2023.107896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/10/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023] Open
Abstract
An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems.
Collapse
Affiliation(s)
- Helong Yu
- College of Information Technology, Jilin Agricultural University, Changchun 130118, China
| | - Zisong Zhao
- College of Information Technology, Jilin Agricultural University, Changchun 130118, China
| | - Ali Asghar Heidari
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Li Ma
- College of Information Technology, Jilin Agricultural University, Changchun 130118, China
| | - Monia Hamdi
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| |
Collapse
|
11
|
Lemus-Romani J, Crawford B, Cisternas-Caneo F, Soto R, Becerra-Rozas M. Binarization of Metaheuristics: Is the Transfer Function Really Important? Biomimetics (Basel) 2023; 8:400. [PMID: 37754151 PMCID: PMC10526273 DOI: 10.3390/biomimetics8050400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023] Open
Abstract
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.
Collapse
Affiliation(s)
- José Lemus-Romani
- Escuela de Construcción Civil, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile
| | - Broderick Crawford
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| | - Felipe Cisternas-Caneo
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| | - Ricardo Soto
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| | - Marcelo Becerra-Rozas
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| |
Collapse
|
12
|
Ferahtia S, Houari A, Rezk H, Djerioui A, Machmoum M, Motahhir S, Ait-Ahmed M. Red-tailed hawk algorithm for numerical optimization and real-world problems. Sci Rep 2023; 13:12950. [PMID: 37558724 PMCID: PMC10412609 DOI: 10.1038/s41598-023-38778-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/14/2023] [Indexed: 08/11/2023] Open
Abstract
This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.
Collapse
Affiliation(s)
- Seydali Ferahtia
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
- Laboratoire de Génie Electrique, Dept. of Electrical Engineering, University of M'sila, M'sila, Algeria
| | - Azeddine Houari
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
| | - Hegazy Rezk
- College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Ali Djerioui
- Laboratoire de Génie Electrique, Dept. of Electrical Engineering, University of M'sila, M'sila, Algeria
| | - Mohamed Machmoum
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
| | - Saad Motahhir
- ENSA, University of Sidi Mohamed Ben Abdellah, Fez, Morocco.
| | - Mourad Ait-Ahmed
- Institut de Recherche en Énergie Électrique de Nantes Atlantique, IREENA, Nantes University, Saint-Nazaire, France
| |
Collapse
|
13
|
Abd El-Mageed AA, Abohany AA, Elashry A. Effective Feature Selection Strategy for Supervised Classification based on an Improved Binary Aquila Optimization Algorithm. COMPUTERS & INDUSTRIAL ENGINEERING 2023; 181:109300. [DOI: 10.1016/j.cie.2023.109300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
14
|
Abd Elaziz M, Ouadfel S, Ibrahim RA. Boosting capuchin search with stochastic learning strategy for feature selection. Neural Comput Appl 2023; 35:14061-14080. [DOI: 10.1007/s00521-023-08400-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/13/2023] [Indexed: 09/02/2023]
Abstract
AbstractThe technological revolution has made available a large amount of data with many irrelevant and noisy features that alter the analysis process and increase time processing. Therefore, feature selection (FS) approaches are used to select the smallest subset of relevant features. Feature selection is viewed as an optimization process for which meta-heuristics have been successfully applied. Thus, in this paper, a new feature selection approach is proposed based on an enhanced version of the Capuchin search algorithm (CapSA). In the developed FS approach, named ECapSA, three modifications have been introduced to avoid a lack of diversity, and premature convergence of the basic CapSA: (1) The inertia weight is adjusted using the logistic map, (2) sine cosine acceleration coefficients are added to improve convergence, and (3) a stochastic learning strategy is used to add more diversity to the movement of Capuchin and a levy random walk. To demonstrate the performance of ECapSA, different datasets are used, and it is compared with other well-known FS methods. The results provide evidence of the superiority of ECapSA among the tested datasets and competitive methods in terms of performance metrics.
Collapse
|
15
|
Mostafa RR, Gaheen MA, Abd ElAziz M, Al-Betar MA, Ewees AA. An improved gorilla troops optimizer for global optimization problems and feature selection. Knowl Based Syst 2023; 269:110462. [DOI: 10.1016/j.knosys.2023.110462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
|
16
|
Xu M, Song Q, Xi M, Zhou Z. Binary arithmetic optimization algorithm for feature selection. Soft comput 2023; 27:1-35. [PMID: 37362265 PMCID: PMC10191101 DOI: 10.1007/s00500-023-08274-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm.
Collapse
Affiliation(s)
- Min Xu
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
| | - Qixian Song
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
| | - Mingyang Xi
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
| | - Zhaorong Zhou
- School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China
- Meteorological Information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes, Chengdu University of Information Technology, Chengdu, 610225 Sichuan China
| |
Collapse
|
17
|
Xie X, Xia F, Wu Y, Liu S, Yan K, Xu H, Ji Z. A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0039. [PMID: 37228513 PMCID: PMC10204742 DOI: 10.34133/plantphenomics.0039] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/28/2023] [Indexed: 05/27/2023]
Abstract
Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.
Collapse
Affiliation(s)
- Xiaojun Xie
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Fei Xia
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Yufeng Wu
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Shouyang Liu
- Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Ke Yan
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| |
Collapse
|
18
|
Bekhouche M, Haouassi H, Bakhouche A, Rahab H, Mahdaoui R. Improved binary crocodiles hunting strategy optimization for feature selection in sentiment analysis. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Feature Selection (FS) for Sentiment Analysis (SA) becomes a complex problem because of the large-sized learning datasets. However, to reduce the data dimensionality, researchers have focused on FS using swarm intelligence approaches that reflect the best classification performance. Crocodiles Hunting Strategy (CHS), a novel swarm-based meta-heuristic that simulates the crocodiles’ hunting behaviour, has demonstrated excellent optimization results. Hence, in this work, two FS algorithms, i.e., Binary CHS (BCHS) and Improved BCHS (IBCHS) based on original CHS were applied for FS in the SA field. In IBCHS, the opposition-based learning technique is applied in the initialization and displacement phases to enhance the search space exploration ability of the IBCHS. The two proposed approaches were evaluated using six well-known corpora in the SA area (Semeval-2016, Semeval-2017, Sanders, Stanford, PMD, and MRD). The obtained result showed that IBCHS outperformed BCHS regarding search capability and convergence speed. The comparison results of IBCHS to several recent state-of-the-art approaches show that IBCHS surpassed other approaches in almost all used corpora. The comprehensive results reveal that the use of OBL in BCHS greatly impacts the performance of BCHS by enhancing the diversity of the population and the exploitation ability, which improves the convergence of the IBCHS.
Collapse
Affiliation(s)
- Maamar Bekhouche
- ICOSI Laboratory, Department of Mathematics and Computer Science, Abbes Laghrour University, Houria, Khenchela, Algeria
| | - Hichem Haouassi
- ICOSI Laboratory, Department of Mathematics and Computer Science, Abbes Laghrour University, Houria, Khenchela, Algeria
| | - Abdelaali Bakhouche
- ICOSI Laboratory, Department of Mathematics and Computer Science, Abbes Laghrour University, Houria, Khenchela, Algeria
| | - Hichem Rahab
- ICOSI Laboratory, Department of Mathematics and Computer Science, Abbes Laghrour University, Houria, Khenchela, Algeria
| | - Rafik Mahdaoui
- ICOSI Laboratory, Department of Mathematics and Computer Science, Abbes Laghrour University, Houria, Khenchela, Algeria
| |
Collapse
|
19
|
Cai C, Gou B, Khishe M, Mohammadi M, Rashidi S, Moradpour R, Mirjalili S. Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:119206. [PMID: 36348736 DOI: 10.1016/j.eswa.2020.113338] [Citation(s) in RCA: 161] [Impact Index Per Article: 161.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 05/25/2023]
Abstract
Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
Collapse
Affiliation(s)
- Chengfeng Cai
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bingchen Gou
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Mohammad Khishe
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Shima Rashidi
- Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
| | - Reza Moradpour
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| |
Collapse
|
20
|
Tran-Ngoc H, Le-Xuan T, Khatir S, De Roeck G, Bui-Tien T, Abdel Wahab M. A promising approach using Fibonacci sequence-based optimization algorithms and advanced computing. Sci Rep 2023; 13:3405. [PMID: 36854757 PMCID: PMC9974976 DOI: 10.1038/s41598-023-28367-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/17/2023] [Indexed: 03/03/2023] Open
Abstract
In this paper, the feasibility of Structural Health Monitoring (SHM) employing a novel Fibonacy Sequence (FS)-based Optimization Algorithms (OAs) and up-to-date computing techniques is investigated for a large-scale railway bridge. During recent decades, numerous metaheuristic intelligent OAs have been proposed and immediately gained a lot of momentum. However, the major concern is how to employ OAs to deal with real-world problems, especially the SHM of large-scale structures. In addition to the requirement of high accuracy, a high computational cost is putting up a major barrier to the real application of OAs. Therefore, this article aims at addressing these two aforementioned issues. First, we propose employing the optimal ability of the golden ratio formulated by the well-known FS to remedy the shortcomings and improve the accuracy of OAs, specifically, a recently proposed new algorithm, namely Salp Swarm Algorithm (SSA). On the other hand, to deal with the high computational cost problems of OAs, we propose employing an up-to-date computing technique, termed superscalar processor to conduct a series of iterations in parallel. Moreover, in this work, the vectorization technique is also applied to reduce the size of the data. The obtained results show that the proposed approach is highly potential to apply for SHM of real large-scale structures.
Collapse
Affiliation(s)
- H. Tran-Ngoc
- grid.444929.60000 0004 0566 7437Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam
| | - T. Le-Xuan
- grid.444929.60000 0004 0566 7437Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam
| | - S. Khatir
- grid.445116.30000 0004 6020 788XFaculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
| | - G. De Roeck
- grid.5596.f0000 0001 0668 7884Department of Civil Engineering, KU Leuven, 3001 Leuven, Belgium
| | - T. Bui-Tien
- grid.444929.60000 0004 0566 7437Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam
| | - Magd Abdel Wahab
- Soete Laboratory, Department of Electrical Energy, Metals, Mechanical Constructions, and Systems, Faculty of Engineering and Architecture, Ghent University, 9000, Gent, Belgium.
| |
Collapse
|
21
|
Alzaqebah A, Al-Kadi O, Aljarah I. An enhanced Harris hawk optimizer based on extreme learning machine for feature selection. PROGRESS IN ARTIFICIAL INTELLIGENCE 2023. [DOI: 10.1007/s13748-023-00298-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
|
22
|
Sadeghian Z, Akbari E, Nematzadeh H, Motameni H. A review of feature selection methods based on meta-heuristic algorithms. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2183267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Affiliation(s)
- Zohre Sadeghian
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Ebrahim Akbari
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Hossein Nematzadeh
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Homayun Motameni
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| |
Collapse
|
23
|
Devi RM, Premkumar M, Kiruthiga G, Sowmya R. IGJO: An Improved Golden Jackel Optimization Algorithm Using Local Escaping Operator for Feature Selection Problems. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11146-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
|
24
|
Mafarja M, Thaher T, Al-Betar MA, Too J, Awadallah MA, Abu Doush I, Turabieh H. Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. APPL INTELL 2023; 53:1-43. [PMID: 36785593 PMCID: PMC9909674 DOI: 10.1007/s10489-022-04427-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/23/2022] [Indexed: 02/11/2023]
Abstract
Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.
Collapse
Affiliation(s)
- Majdi Mafarja
- Department of Computer Science, Birzeit University, Birzeit, Palestine
| | - Thaer Thaher
- Department of Computer Systems Engineering, Arab American University, Jenin, Palestine
- Information Technology Engineering, Al-Quds University, Abu Dies, Jerusalem, Palestine
| | - Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesDeepSinghML2017, Irbid, Jordan
| | - Jingwei Too
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal Melaka, Malaysia
| | - Mohammed A. Awadallah
- Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates
| | - Iyad Abu Doush
- Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait
- Computer Science Department, Yarmouk University, Irbid, Jordan
| | - Hamza Turabieh
- Department of Health Management and Informatics, University of Missouri, Columbia, 5 Hospital Drive, Columbia, MO 65212 USA
| |
Collapse
|
25
|
Sun L, Si S, Ding W, Xu J, Zhang Y. BSSFS: binary sparrow search algorithm for feature selection. INT J MACH LEARN CYB 2023. [DOI: 10.1007/s13042-023-01788-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
26
|
Information entropy-based differential evolution with extremely randomized trees and LightGBM for protein structural class prediction. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
27
|
A hierarchical intrusion detection system based on extreme learning machine and nature-inspired optimization. Comput Secur 2023. [DOI: 10.1016/j.cose.2022.102957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
|
28
|
Ewees AA, Al-qaness MAA, Abualigah L, Algamal ZY, Oliva D, Yousri D, Elaziz MA. Enhanced feature selection technique using slime mould algorithm: a case study on chemical data. Neural Comput Appl 2023; 35:3307-3324. [PMID: 36245794 PMCID: PMC9547998 DOI: 10.1007/s00521-022-07852-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 09/16/2022] [Indexed: 01/31/2023]
Abstract
Feature selection techniques are considered one of the most important preprocessing steps, which has the most significant influence on the performance of data analysis and decision making. These FS techniques aim to achieve several objectives (such as reducing classification error and minimizing the number of features) at the same time to increase the classification rate. FS based on Metaheuristic (MH) is considered one of the most promising techniques to improve the classification process. This paper presents a modified method of the Slime mould algorithm depending on the Marine Predators Algorithm (MPA) operators as a local search strategy, which leads to increasing the convergence rate of the developed method, named SMAMPA and avoiding the attraction to local optima. The efficiency of SMAMPA is evaluated using twenty datasets and compared its results with the state-of-the-art FS methods. In addition, the applicability of SMAMPA to work with real-world problems is evaluated by using it as a quantitative structure-activity relationship (QSAR) model. The obtained results show the high ability of the developed SMAMPA method to reduce the dimension of the tested datasets by increasing the prediction rate. In addition, it provides results better than other FS techniques in terms of performance metrics.
Collapse
Affiliation(s)
- Ahmed A. Ewees
- Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha, 61922 Saudi Arabia ,Department of Computer, Damietta University, Damietta, 34517 Egypt
| | - Mohammed A. A. Al-qaness
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004 China
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan ,Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
| | - Zakariya Yahya Algamal
- Department of Statistics and Informatics, University of Mosul, Mosul, Iraq ,College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
| | - Diego Oliva
- Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal Mexico
| | - Dalia Yousri
- Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519 Egypt ,Faculty of Computer Science and Engineering, Galala University, Suez, Egypt ,Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE ,Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| |
Collapse
|
29
|
Qaraad M, Amjad S, Hussein NK, Mirjalili S, Elhosseini MA. An innovative time-varying particle swarm-based Salp algorithm for intrusion detection system and large-scale global optimization problems. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10322-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
|
30
|
Wani JA, Ganaie SA. The scientific outcome in the domain of grey literature: bibliometric mapping and visualisation using the R-bibliometrix package and the VOSviewer. LIBRARY HI TECH 2022. [DOI: 10.1108/lht-01-2022-0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PurposeThe current study aims to map the scientific output of grey literature (GL) through bibliometric approaches.Design/methodology/approachThe source for data extraction is a comprehensive “indexing and abstracting” database, “Web of Science” (WOS). A lexical title search was applied to get the corpus of the study – a total of 4,599 articles were extracted for data analysis and visualisation. Further, the data were analysed by using the data analytical tools, R-studio and VOSViewer.FindingsThe findings showed that the “publications” have substantially grown up during the timeline. The most productive phase (2018–2021) resulted in 47% of articles. The prominent sources were PLOS One and NeuroImage. The highest number of papers were contributed by Haddaway and Kumar. The most relevant countries were the USA and UK.Practical implicationsThe study is useful for researchers interested in the GL research domain. The study helps to understand the evolution of the GL to provide research support further in this area.Originality/valueThe present study provides a new orientation to the scholarly output of the GL. The study is rigorous and all-inclusive based on analytical operations like the research networks, collaboration and visualisation. To the best of the authors' knowledge, this manuscript is original, and no similar works have been found with the research objectives included here.
Collapse
|
31
|
Rahab H, Haouassi H, Souidi MEH, Bakhouche A, Mahdaoui R, Bekhouche M. A Modified Binary Rat Swarm Optimization Algorithm for Feature Selection in Arabic Sentiment Analysis. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07466-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
32
|
Wang Z, Ding H, Yang J, Hou P, Dhiman G, Wang J, Yang Z, Li A. Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization. Front Bioeng Biotechnol 2022; 10:1018895. [PMID: 36532584 PMCID: PMC9751665 DOI: 10.3389/fbioe.2022.1018895] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/17/2022] [Indexed: 09/28/2023] Open
Abstract
Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.
Collapse
Affiliation(s)
- Zongshan Wang
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Hongwei Ding
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Jingjing Yang
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Peng Hou
- School of Computer Science, Fudan University, Shanghai, China
| | - Gaurav Dhiman
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Gharuan, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India
| | - Jie Wang
- School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhijun Yang
- School of Information Science and Engineering, Yunnan University, Kunming, China
- University Key Laboratory of Internet of Things Technology and Application, Kunming, China
| | - Aishan Li
- Rackham Graduate School, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
33
|
Wang X, Dong X, Zhang Y, Chen H. Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection. JOURNAL OF BIONIC ENGINEERING 2022; 20:1153-1174. [PMID: 36466727 PMCID: PMC9709762 DOI: 10.1007/s42235-022-00298-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum; the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend; and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems' dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization; for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s42235-022-00298-7.
Collapse
Affiliation(s)
- Xin Wang
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, 130012 China
| | - Xiaogang Dong
- School of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, 130012 China
| | - Yanan Zhang
- School of Management, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049 China
- Information Construction Office, Changchun University of Technology, Changchun, Jilin, 130012 China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| |
Collapse
|
34
|
Pan JS, Hu P, Snášel V, Chu SC. A survey on binary metaheuristic algorithms and their engineering applications. Artif Intell Rev 2022; 56:6101-6167. [PMID: 36466763 PMCID: PMC9684803 DOI: 10.1007/s10462-022-10328-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.
Collapse
Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 Shandong China
- Department of Information Management, Chaoyang University of Technology, Taichung, 413310 Taiwan
| | - Pei Hu
- Department of Information Management, Chaoyang University of Technology, Taichung, 413310 Taiwan
- School of Computer Science and Software Engineering, Nanyang Institute of Technology, Nanyang, 473004 Henan China
| | - Václav Snášel
- Faculty of Electrical Engineering and Computer Science, VŠB—Technical University of Ostrava, Ostrava, 70032 Moravskoslezský kraj Czech Republic
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 Shandong China
| |
Collapse
|
35
|
Equilibrium optimizer with divided population based on distance and its application in feature selection problems. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
36
|
An aphid inspired metaheuristic optimization algorithm and its application to engineering. Sci Rep 2022; 12:18064. [PMID: 36302816 PMCID: PMC9613887 DOI: 10.1038/s41598-022-22170-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/11/2022] [Indexed: 01/24/2023] Open
Abstract
The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphids with wings, including the generation of winged aphids, flight mood, and attack mood. Concurrently, the corresponding optimization models are presented according to the above phases. At the phase of the flight mood, according to the comprehensive influence of energy and the airflow, the individuals adaptively choose the flight mode to migrate; at the phase of attack mood, individuals use their sense of smell and vision to locate food sources for movement. Experiments on benchmark test functions and two classical engineering design problems, indicate that the desired AOA is more efficient than other metaheuristic algorithms.
Collapse
|
37
|
Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00882-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractFor incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimistic neighborhood multi-granulation tolerance joint entropy (PTSIJE), which can be used to incomplete neighborhood decision systems. First, from the algebra view, four kinds of neighborhood multi-granulation self-information measures of decision variables are proposed by using the upper and lower approximations of NMRS. We discuss the related properties, and find the fourth measure-lenient neighborhood multi-granulation self-information measure (NMSI) has better classification performance. Then, inspired by the algebra and information views simultaneously, a feature selection method based on PTSIJE is proposed. Finally, the Fisher score method is used to delete uncorrelated features to reduce the computational complexity for high-dimensional gene datasets, and a heuristic feature selection algorithm is raised to improve classification performance for mixed and incomplete datasets. Experimental results on 11 datasets show that our method selects fewer features and has higher classification accuracy than related methods.
Collapse
|
38
|
An Efficient High-dimensional Feature Selection Approach Driven By Enhanced Multi-strategy Grey Wolf Optimizer for Biological Data Classification. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07836-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
|
39
|
Yi Z, Yangkun Z, Hongda Y, Hong W. Application of an improved Discrete Salp Swarm Algorithm to the wireless rechargeable sensor network problem. Front Bioeng Biotechnol 2022; 10:923798. [PMID: 36204468 PMCID: PMC9531118 DOI: 10.3389/fbioe.2022.923798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
This paper presents an improved Discrete Salp Swarm Algorithm based on the Ant Colony System (DSSACS). Firstly, we use the Ant Colony System (ACS) to optimize the initialization of the salp colony and discretize the algorithm, then use the crossover operator and mutation operator to simulate the foraging behavior of the followers in the salp colony. We tested DSSACS with several algorithms on the TSP dataset. For TSP files of different sizes, the error of DSSACS is generally between 0.78% and 2.95%, while other algorithms are generally higher than 2.03%, or even 6.43%. The experiments show that our algorithm has a faster convergence speed, better positive feedback mechanism, and higher accuracy. We also apply the new algorithm for the Wireless rechargeable sensor network (WRSN) problem. For the selection of the optimal path, the path selected by DSSACS is always about 20% shorter than the path selected by ACS. Results show that DSSACS has obvious advantages over other algorithms in MCV’s multi-path planning and saves more time and economic cost than other swarm intelligence algorithms in the wireless rechargeable sensor network.
Collapse
Affiliation(s)
- Zhang Yi
- College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China
| | - Zhou Yangkun
- College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China
| | - Yu Hongda
- College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China
| | - Wang Hong
- Information Center of the Ministry of Natural Resources, Beijing, China
- *Correspondence: Wang Hong,
| |
Collapse
|
40
|
Hybrid binary COOT algorithm with simulated annealing for feature selection in high-dimensional microarray data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07780-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
41
|
Improved firefly algorithm for feature selection with the ReliefF-based initialization and the weighted voting mechanism. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07755-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
42
|
Panwar K, Deep K. Discrete Salp Swarm Algorithm for Euclidean Travelling Salesman Problem. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03976-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
43
|
Beheshti Z. BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
44
|
Morteza Ghazali S, Alizadeh M, Mazloum J, Baleghi Y. Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
45
|
Song XF, Zhang Y, Gong DW, Gao XZ. A Fast Hybrid Feature Selection Based on Correlation-Guided Clustering and Particle Swarm Optimization for High-Dimensional Data. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:9573-9586. [PMID: 33729976 DOI: 10.1109/tcyb.2021.3061152] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The "curse of dimensionality" and the high computational cost have still limited the application of the evolutionary algorithm in high-dimensional feature selection (FS) problems. This article proposes a new three-phase hybrid FS algorithm based on correlation-guided clustering and particle swarm optimization (PSO) (HFS-C-P) to tackle the above two problems at the same time. To this end, three kinds of FS methods are effectively integrated into the proposed algorithm based on their respective advantages. In the first and second phases, a filter FS method and a feature clustering-based method with low computational cost are designed to reduce the search space used by the third phase. After that, the third phase applies oneself to finding an optimal feature subset by using an evolutionary algorithm with the global searchability. Moreover, a symmetric uncertainty-based feature deletion method, a fast correlation-guided feature clustering strategy, and an improved integer PSO are developed to improve the performance of the three phases, respectively. Finally, the proposed algorithm is validated on 18 publicly available real-world datasets in comparison with nine FS algorithms. Experimental results show that the proposed algorithm can obtain a good feature subset with the lowest computational cost.
Collapse
|
46
|
Zou L, Zhou S, Li X. An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection. ENTROPY 2022; 24:e24081065. [PMID: 36010729 PMCID: PMC9407072 DOI: 10.3390/e24081065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/21/2022] [Accepted: 07/30/2022] [Indexed: 01/27/2023]
Abstract
To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.
Collapse
|
47
|
A binary dandelion algorithm using seeding and chaos population strategies for feature selection. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
48
|
A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem. MATHEMATICS 2022. [DOI: 10.3390/math10152675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.
Collapse
|
49
|
Luo S, Jiang X, He Y, Li J, Jiao W, Zhang S, Xu F, Han Z, Sun J, Yang J, Wang X, Ma X, Lin Z. Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery. FRONTIERS IN PLANT SCIENCE 2022; 13:948249. [PMID: 35968116 PMCID: PMC9372391 DOI: 10.3389/fpls.2022.948249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.
Collapse
Affiliation(s)
- Shanjun Luo
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Xueqin Jiang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Yingbin He
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China
| | - Jianping Li
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Weihua Jiao
- Center for Agricultural and Rural Economic Research, Shandong University of Finance and Economics, Jinan, China
| | - Shengli Zhang
- Potato Science Institute, Jilin Academy of Vegetables and Flower Sciences, Changchun, China
| | - Fei Xu
- Potato Science Institute, Jilin Academy of Vegetables and Flower Sciences, Changchun, China
| | - Zhongcai Han
- Potato Science Institute, Jilin Academy of Vegetables and Flower Sciences, Changchun, China
| | - Jing Sun
- Potato Science Institute, Jilin Academy of Vegetables and Flower Sciences, Changchun, China
| | - Jinpeng Yang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiangyi Wang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xintian Ma
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zeru Lin
- School of Economics and Management, Tiangong University, Tianjin, China
| |
Collapse
|
50
|
Nadimi-Shahraki MH, Zamani H, Mirjalili S. Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Comput Biol Med 2022; 148:105858. [PMID: 35868045 DOI: 10.1016/j.compbiomed.2022.105858] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 06/15/2022] [Accepted: 07/08/2022] [Indexed: 01/01/2023]
Abstract
The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.
Collapse
Affiliation(s)
- Mohammad H Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia.
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
| |
Collapse
|