Multi-strategy improved salp swarm algorithm and its application in reliability optimization
To improve the convergence speed and solution precision of the standard Salp Swarm Algorithm (SSA), a hybrid Salp Swarm Algorithm based on Dimension-by-dimension Centroid Opposition-based learning strategy, Random factor and Particle Swarm Optimization's social learning strategy (DCORSSA-PSO) i...
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-03-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022247?viewType=HTML |
| _version_ | 1818027981326516224 |
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| author | Dongning Chen Jianchang Liu Chengyu Yao Ziwei Zhang Xinwei Du |
| author_facet | Dongning Chen Jianchang Liu Chengyu Yao Ziwei Zhang Xinwei Du |
| author_sort | Dongning Chen |
| collection | DOAJ |
| description | To improve the convergence speed and solution precision of the standard Salp Swarm Algorithm (SSA), a hybrid Salp Swarm Algorithm based on Dimension-by-dimension Centroid Opposition-based learning strategy, Random factor and Particle Swarm Optimization's social learning strategy (DCORSSA-PSO) is proposed. Firstly, a dimension-by-dimension centroid opposition-based learning strategy is added in the food source update stage of SSA to increase the population diversity and reduce the inter-dimensional interference. Secondly, in the followers' position update equation of SSA, constant 1 is replaced by a random number between 0 and 1 to increase the randomness of the search and the ability to jump out of local optima. Finally, the social learning strategy of PSO is also added to the followers' position update equation to accelerate the population convergence. The statistical results on ten classical benchmark functions by the Wilcoxon test and Friedman test show that compared with SSA and other well-known optimization algorithms, the proposed DCORSSA-PSO has significantly improved the precision of the solution and the convergence speed, as well as its robustness. The DCORSSA-PSO is applied to system reliability optimization design based on the T-S fault tree. The simulation results show that the failure probability of the designed system under the cost constraint is less than other algorithms, which illustrates that the application of DCORSSA-PSO can effectively improve the design level of reliability optimization. |
| first_indexed | 2024-12-10T04:56:32Z |
| format | Article |
| id | doaj.art-e55490d3844a4dd79d91fd776c6f4c24 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-10T04:56:32Z |
| publishDate | 2022-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-e55490d3844a4dd79d91fd776c6f4c242022-12-22T02:01:30ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-03-011955269529210.3934/mbe.2022247Multi-strategy improved salp swarm algorithm and its application in reliability optimizationDongning Chen 0Jianchang Liu 1Chengyu Yao2Ziwei Zhang 3Xinwei Du41. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China 2. Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China 2. Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China3. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China 2. Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, China1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004, China 2. Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004, ChinaTo improve the convergence speed and solution precision of the standard Salp Swarm Algorithm (SSA), a hybrid Salp Swarm Algorithm based on Dimension-by-dimension Centroid Opposition-based learning strategy, Random factor and Particle Swarm Optimization's social learning strategy (DCORSSA-PSO) is proposed. Firstly, a dimension-by-dimension centroid opposition-based learning strategy is added in the food source update stage of SSA to increase the population diversity and reduce the inter-dimensional interference. Secondly, in the followers' position update equation of SSA, constant 1 is replaced by a random number between 0 and 1 to increase the randomness of the search and the ability to jump out of local optima. Finally, the social learning strategy of PSO is also added to the followers' position update equation to accelerate the population convergence. The statistical results on ten classical benchmark functions by the Wilcoxon test and Friedman test show that compared with SSA and other well-known optimization algorithms, the proposed DCORSSA-PSO has significantly improved the precision of the solution and the convergence speed, as well as its robustness. The DCORSSA-PSO is applied to system reliability optimization design based on the T-S fault tree. The simulation results show that the failure probability of the designed system under the cost constraint is less than other algorithms, which illustrates that the application of DCORSSA-PSO can effectively improve the design level of reliability optimization.https://www.aimspress.com/article/doi/10.3934/mbe.2022247?viewType=HTMLsalp swarm algorithmsocial learningcentroid opposition-based learningsystem reliability optimizationt-s fault tree |
| spellingShingle | Dongning Chen Jianchang Liu Chengyu Yao Ziwei Zhang Xinwei Du Multi-strategy improved salp swarm algorithm and its application in reliability optimization Mathematical Biosciences and Engineering salp swarm algorithm social learning centroid opposition-based learning system reliability optimization t-s fault tree |
| title | Multi-strategy improved salp swarm algorithm and its application in reliability optimization |
| title_full | Multi-strategy improved salp swarm algorithm and its application in reliability optimization |
| title_fullStr | Multi-strategy improved salp swarm algorithm and its application in reliability optimization |
| title_full_unstemmed | Multi-strategy improved salp swarm algorithm and its application in reliability optimization |
| title_short | Multi-strategy improved salp swarm algorithm and its application in reliability optimization |
| title_sort | multi strategy improved salp swarm algorithm and its application in reliability optimization |
| topic | salp swarm algorithm social learning centroid opposition-based learning system reliability optimization t-s fault tree |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022247?viewType=HTML |
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