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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.
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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
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2
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Lin Y, Heidari AA, Wang S, Chen H, Zhang Y. An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems. Biomimetics (Basel) 2023; 8:441. [PMID: 37754192 PMCID: PMC10526405 DOI: 10.3390/biomimetics8050441] [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: 07/10/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.
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Affiliation(s)
- Yaoyao Lin
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China; (Y.L.); (A.A.H.)
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China; (Y.L.); (A.A.H.)
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China; (Y.L.); (A.A.H.)
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
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3
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A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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4
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Elaziz MA, Ahmadein M, Ataya S, Alsaleh N, Forestiero A, Elsheikh AH. A Quantum-Based Chameleon Swarm for Feature Selection. MATHEMATICS 2022; 10:3606. [DOI: 10.3390/math10193606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.
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5
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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]
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6
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Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1825341. [PMID: 36072739 PMCID: PMC9441366 DOI: 10.1155/2022/1825341] [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: 06/29/2022] [Revised: 08/07/2022] [Accepted: 08/09/2022] [Indexed: 12/02/2022]
Abstract
With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).
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7
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An Efficient Heap Based Optimizer Algorithm for Feature Selection. MATHEMATICS 2022. [DOI: 10.3390/math10142396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.
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Kassaymeh S, Abdullah S, Al-Betar MA, Alweshah M. Salp swarm optimizer for modeling the software fault prediction problem. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2021.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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9
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Rashno A, Shafipour M, Fadaei S. Particle ranking: An Efficient Method for Multi-Objective Particle Swarm Optimization Feature Selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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10
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Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria. ENERGIES 2022. [DOI: 10.3390/en15103579] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.
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11
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Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108511] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Kareem SS, Mostafa RR, Hashim FA, El-Bakry HM. An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection. SENSORS 2022; 22:s22041396. [PMID: 35214297 PMCID: PMC8962996 DOI: 10.3390/s22041396] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/06/2022] [Accepted: 02/07/2022] [Indexed: 02/01/2023]
Abstract
The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.
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Affiliation(s)
- Saif S. Kareem
- Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt; (S.S.K.); (H.M.E.-B.)
| | - Reham R. Mostafa
- Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt; (S.S.K.); (H.M.E.-B.)
- Correspondence:
| | - Fatma A. Hashim
- Faculty of Engineering, Helwan University, Cairo 11795, Egypt;
| | - Hazem M. El-Bakry
- Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt; (S.S.K.); (H.M.E.-B.)
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13
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Ehteram M, Panahi F, Ahmed AN, Huang YF, Kumar P, Elshafie A. Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:10675-10701. [PMID: 34528189 DOI: 10.1007/s11356-021-16301-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/29/2021] [Indexed: 06/13/2023]
Abstract
Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.
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Affiliation(s)
- Mohammad Ehteram
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Fatemeh Panahi
- Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering,, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia
| | - Yuk Feng Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia
| | - Pavitra Kumar
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia.
| | - Ahmed Elshafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
- National Water and Energy Center, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates
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14
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A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem. MATHEMATICS 2022. [DOI: 10.3390/math10030464] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This survey is an effort to provide a research repository and a useful reference for researchers to guide them when planning to develop new Nature-inspired Algorithms tailored to solve Feature Selection problems (NIAs-FS). We identified and performed a thorough literature review in three main streams of research lines: Feature selection problem, optimization algorithms, particularly, meta-heuristic algorithms, and modifications applied to NIAs to tackle the FS problem. We provide a detailed overview of 156 different articles about NIAs modifications for tackling FS. We support our discussions by analytical views, visualized statistics, applied examples, open-source software systems, and discuss open issues related to FS and NIAs. Finally, the survey summarizes the main foundations of NIAs-FS with approximately 34 different operators investigated. The most popular operator is chaotic maps. Hybridization is the most widely used modification technique. There are three types of hybridization: Integrating NIA with another NIA, integrating NIA with a classifier, and integrating NIA with a classifier. The most widely used hybridization is the one that integrates a classifier with the NIA. Microarray and medical applications are the dominated applications where most of the NIA-FS are modified and used. Despite the popularity of the NIAs-FS, there are still many areas that need further investigation.
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15
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A comprehensive review of deep neuro-fuzzy system architectures and their optimization methods. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06807-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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16
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Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06751-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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17
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Hu G, Du B, Wang X, Wei G. An enhanced black widow optimization algorithm for feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107638] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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18
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Zhang Q, Wang Z, Heidari AA, Gui W, Shao Q, Chen H, Zaguia A, Turabieh H, Chen M. Gaussian Barebone Salp Swarm Algorithm with Stochastic Fractal Search for medical image segmentation: A COVID-19 case study. Comput Biol Med 2021; 139:104941. [PMID: 34801864 DOI: 10.1016/j.compbiomed.2021.104941] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/11/2021] [Accepted: 10/11/2021] [Indexed: 01/11/2023]
Abstract
An appropriate threshold is a key to using the multi-threshold segmentation method to solve image segmentation problems, and the swarm intelligence (SI) optimization algorithm is one of the popular methods to obtain the optimal threshold. Moreover, Salp Swarm Algorithm (SSA) is a recently released swarm intelligent optimization algorithm. Compared with other SI optimization algorithms, the optimization solution strategy of the SSA still needs to be improved to enhance further the solution accuracy and optimization efficiency of the algorithm. Accordingly, this paper designs an effective segmentation method based on a non-local mean 2D histogram and 2D Kapur's entropy called SSA with Gaussian Barebone and Stochastic Fractal Search (GBSFSSSA) by combining Gaussian Barebone and Stochastic Fractal Search mechanism. In GBSFSSSA, the Gaussian Barebone and Stochastic Fractal Search mechanism effectively balance the global search ability and local search ability of the basic SSA. The CEC2017 competition data set is used to prove the algorithm's performance, and GBSFSSSA shows an absolute advantage over some typical competitive algorithms. Furthermore, the algorithm is applied in image segmentation of COVID-19 CT images, and the results are analyzed based on three different metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM), which can lead to the conclusion that the overall performance of GBSFSSSA is better than the comparison algorithm and can effectively improve the segmentation of medical images. Therefore, it is justified that GBSFSSSA is a reliable and effective method in solving the multi-threshold image segmentation problem.
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Affiliation(s)
- Qian Zhang
- Wenzhou University of Technology, Wenzhou, 325035, China.
| | - Zhiyan Wang
- School of Artificial Intelligence, Jilin International Studies University, Changchun, 130000, China.
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Wenyong Gui
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Qike Shao
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif, 21944, Saudi Arabia.
| | - Hamza Turabieh
- Department of Information Technology, College of Computers and Information Technology, PO Box 11099, Taif, 21944, Saudi Arabia.
| | - Mayun Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Zhao S, Wang P, Heidari AA, Chen H, He W, Xu S. Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy. Comput Biol Med 2021; 139:105015. [PMID: 34800808 DOI: 10.1016/j.compbiomed.2021.105015] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 11/30/2022]
Abstract
Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com.
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Affiliation(s)
- Songwei Zhao
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Pengjun Wang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Wenming He
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.
| | - Suling Xu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.
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20
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Hu J, Heidari AA, Zhang L, Xue X, Gui W, Chen H, Pan Z. Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection. INT J INTELL SYST 2021. [DOI: 10.1002/int.22744] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Jiao Hu
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Lejun Zhang
- College of Information Engineering Yangzhou University Yangzhou China
| | - Xiao Xue
- College of Computer Science and Technology Henan Polytechnic University Zhengzhou China
| | - Wenyong Gui
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
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21
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Ibrahim RA, Abualigah L, Ewees AA, Al-qaness MAA, Yousri D, Alshathri S, Abd Elaziz M. An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection. ENTROPY 2021; 23:e23091189. [PMID: 34573818 PMCID: PMC8472813 DOI: 10.3390/e23091189] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/05/2021] [Accepted: 09/06/2021] [Indexed: 11/16/2022]
Abstract
With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.
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Affiliation(s)
- Rehab Ali Ibrahim
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; (R.A.I.); (M.A.E.)
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan;
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt;
| | - Mohammed A. A. Al-qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
| | - Dalia Yousri
- Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt;
| | - Samah Alshathri
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
- Correspondence:
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; (R.A.I.); (M.A.E.)
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
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22
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Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107034] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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23
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Ewees AA, Al-qaness MA, Abd Elaziz M. Enhanced salp swarm algorithm based on firefly algorithm for unrelated parallel machine scheduling with setup times. APPLIED MATHEMATICAL MODELLING 2021; 94:285-305. [DOI: 10.1016/j.apm.2021.01.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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24
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Abd Elaziz M, Yousri D, Mirjalili S. A hybrid Harris hawks-moth-flame optimization algorithm including fractional-order chaos maps and evolutionary population dynamics. ADVANCES IN ENGINEERING SOFTWARE 2021; 154:102973. [DOI: 10.1016/j.advengsoft.2021.102973] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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25
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Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106642] [Citation(s) in RCA: 132] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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26
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Too J, Mirjalili S. A Hyper Learning Binary Dragonfly Algorithm for Feature Selection: A COVID-19 Case Study. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106553] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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