1
|
Zhang L, Chen X. Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm for feature selection. Sci Rep 2024; 14:15413. [PMID: 38965341 PMCID: PMC11224333 DOI: 10.1038/s41598-024-66285-6] [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: 01/19/2024] [Accepted: 07/01/2024] [Indexed: 07/06/2024] Open
Abstract
Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The Chimp Optimisation Algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm's performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.
Collapse
Affiliation(s)
- Li Zhang
- College of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, People's Republic of China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, People's Republic of China.
| | - XiaoBo Chen
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, People's Republic of China
- People's Bank of China Changzhou City Center Branch, Jiangsu, 213001, Changzhou, People's Republic of 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
|
Wang K, Fan X, Yang X, Zhou Z. An AQI decomposition ensemble model based on SSA-LSTM using improved AMSSA-VMD decomposition reconstruction technique. ENVIRONMENTAL RESEARCH 2023:116365. [PMID: 37301497 DOI: 10.1016/j.envres.2023.116365] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/21/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Air quality index (AQI) is a key index for monitoring air pollution and can be used as guide for ensuring good public health. Accurate AQI prediction allows timely control and management of air pollution. In this study, a new integrated learning model was constructed to predict AQI. A smart reverse learning approach based on AMSSA was utilized to increase the diversity of populations, and an improved AMSSA (IAMSSA) was established. The optimum parameters with penalty factor α and mode number K of VMD were obtained using IAMSSA. The IAMSSA-VMD was used to decompose nonlinear and non-stationary AQI information series into several regular and smooth sub-sequences. The Sparrow Search Algorithm (SSA) was used to determine the optimum LSTM parameters. The results showed that: (1) IAMSSA exhibits faster convergence and higher accuracy and stability using simulation experiments compared with seven conventional optimization algorithms in 12 test functions. (2) IAMSSA-VMD was used to decompose the original air quality data results in multiple uncoupled intrinsic mode function (IMF) components and one residual (RES). An SSA-LSTM model was built for each IMF and one RES component, which effectively extracted the predicted values. (3) LSTM, SSA-LSTM, VMD-LSTM, VMD-SSA-LSTM, AMSSA-VMD-SSA-LSTM, and IAMSSA-VMD-SSA-LSTM models were used for prediction of AQI based on data from three cities (Chengdu, Guangzhou, and Shenyang). IAMSSA-VMD-SSA-LSTM exhibited the optimal prediction performance with MAE, RMSE, MAPE, and R2 of 3.692, 4.909, 6.241, and 0.981, respectively. (4) Generalization outcomes revealed that the IAMSSA-VMD-SSA-LSTM model had optimal generalization ability. In summary, the decomposition ensemble model proposed in this study has higher prediction accuracy, improved fitting effect and generalization ability compared with other models. These properties indicate the superiority of the decomposition ensemble model and provides a theoretical and technical basis for prediction of air pollution and ecosystem restoration.
Collapse
Affiliation(s)
- Kai Wang
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China
| | - Xinyue Fan
- College of Management Science, Chengdu University of Technology, Chengdu, 610059, China.
| | - Xiaoyi Yang
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China
| | - Zhongli Zhou
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, 610059, China; College of Management Science, Chengdu University of Technology, Chengdu, 610059, China
| |
Collapse
|
4
|
Pan N, Ye X, Cao J, Zhang J, Han Y, He Z. Optimization of urban emergency support material distribution under major public health emergencies based on improved sparrow search algorithm. Sci Prog 2023; 106:368504231175328. [PMID: 37201921 PMCID: PMC10450338 DOI: 10.1177/00368504231175328] [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] [Indexed: 05/20/2023]
Abstract
The outbreak of major public health emergencies such as the coronavirus epidemic has put forward new requirements for urban emergency management procedures. Accuracy and effective distribution model of emergency support materials, as an effective tool to inhibit the deterioration of the public health sector, have gradually become a research hotspot. The distribution of urban emergency support devices, under the secondary supply chain structure of "material transfer center-demand point," which may involve confusing demands, is studied to determine the actual situation of fuzzy requests under the impact of an epidemic outbreak. An optimization model of urban emergency support material distribution, based on Credibility theory, is first constructed. Then an improved sparrow search algorithm, ISSA, was designed by introducing Sobol sequence, Cauchy variation and bird swarm algorithm into the structure of the classical SSA. In addition, numerical validation and standard test set validation were carried out and the experimental results showed that the introduced improved strategy effectively improved the global search capability of the algorithm. Furthermore, simulation experiments are conducted, based on Shanghai, and the comparison with existing cutting-edge algorithms shows that the designed algorithm has stronger superiority and robustness. And the simulation results show that the designed algorithm can reduce vehicle cost by 4.83%, time cost by 13.80%, etc. compared to other algorithms. Finally, the impact of preference value on the distribution of emergency support materials is analyzed to help decision-makers to develop reasonable and effective distribution strategies according to the impact of major public health emergencies. The results of the study provide a practical reference for the solution of urban emergency support materials distribution problems.
Collapse
Affiliation(s)
- Nan Pan
- Faculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming, China
| | - Xiao Ye
- Faculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming, China
| | - Jianing Cao
- Faculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming, China
| | - Jingcheng Zhang
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Yuhang Han
- Faculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming, China
| | - Zhaolei He
- Measurement Center, Yunnan Power Grid Co., Ltd., Kunming, China
| |
Collapse
|
5
|
Hamed Alnaish ZA, Algamal ZY. Improving binary crow search algorithm for feature selection. JOURNAL OF INTELLIGENT SYSTEMS 2023. [DOI: 10.1515/jisys-2022-0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023] Open
Abstract
Abstract
The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time.
Collapse
Affiliation(s)
| | - Zakariya Yahya Algamal
- Department of Statistics and Informatics, University of Mosul , 41001 Mosul , Iraq
- College of Engineering, University of Warith Al-Anbiyaa , 56001 Karbala , Iraq
| |
Collapse
|
6
|
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]
|