An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization
With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/2/210 |
| _version_ | 1797595904211943424 |
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| author | Jianhui Liang Lifang Wang Miao Ma |
| author_facet | Jianhui Liang Lifang Wang Miao Ma |
| author_sort | Jianhui Liang |
| collection | DOAJ |
| description | With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter <i>G</i> is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance. |
| first_indexed | 2024-03-11T02:44:05Z |
| format | Article |
| id | doaj.art-c18027a99d8e437db362c6dfd8af0f74 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T02:44:05Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-c18027a99d8e437db362c6dfd8af0f742023-11-18T09:29:16ZengMDPI AGBiomimetics2313-76732023-05-018221010.3390/biomimetics8020210An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional OptimizationJianhui Liang0Lifang Wang1Miao Ma2School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710062, ChinaWith the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter <i>G</i> is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.https://www.mdpi.com/2313-7673/8/2/210meta-heuristic optimizationchicken swarm optimizationhigh-dimensional optimization |
| spellingShingle | Jianhui Liang Lifang Wang Miao Ma An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization Biomimetics meta-heuristic optimization chicken swarm optimization high-dimensional optimization |
| title | An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization |
| title_full | An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization |
| title_fullStr | An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization |
| title_full_unstemmed | An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization |
| title_short | An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization |
| title_sort | adaptive dual population collaborative chicken swarm optimization algorithm for high dimensional optimization |
| topic | meta-heuristic optimization chicken swarm optimization high-dimensional optimization |
| url | https://www.mdpi.com/2313-7673/8/2/210 |
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