A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow sear...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/23/11192 |
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| author | Xiaoxu Yang Jie Liu Yi Liu Peng Xu Ling Yu Lei Zhu Huayue Chen Wu Deng |
| author_facet | Xiaoxu Yang Jie Liu Yi Liu Peng Xu Ling Yu Lei Zhu Huayue Chen Wu Deng |
| author_sort | Xiaoxu Yang |
| collection | DOAJ |
| description | Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number <i>t</i> as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems. |
| first_indexed | 2024-03-10T04:57:52Z |
| format | Article |
| id | doaj.art-167edda97c5d49b382d0820da580f9ff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:57:52Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-167edda97c5d49b382d0820da580f9ff2023-11-23T02:04:00ZengMDPI AGApplied Sciences2076-34172021-11-0111231119210.3390/app112311192A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution MutationXiaoxu Yang0Jie Liu1Yi Liu2Peng Xu3Ling Yu4Lei Zhu5Huayue Chen6Wu Deng7College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaAnhui CQC-CHEARI Technology Co., Ltd., Chuzhou 239000, ChinaResearch Center of Big Data and Information Management, Civil Aviation Management Institute of China, Beijing 100102, ChinaChuzhou Technical Supervision and Testing Center, Chuzhou 239000, ChinaChina Household Electric Appliance Research Institute, Beijing 100176, ChinaChina Household Electric Appliance Research Institute, Beijing 100176, ChinaSchool of Computer Science, China West Normal University, Nanchong 637002, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaAiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number <i>t</i> as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.https://www.mdpi.com/2076-3417/11/23/11192sparrow search algorithmchaotic mappingadaptive weightt-distribution mutationsmulti-strategyglobal optimization |
| spellingShingle | Xiaoxu Yang Jie Liu Yi Liu Peng Xu Ling Yu Lei Zhu Huayue Chen Wu Deng A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation Applied Sciences sparrow search algorithm chaotic mapping adaptive weight t-distribution mutations multi-strategy global optimization |
| title | A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation |
| title_full | A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation |
| title_fullStr | A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation |
| title_full_unstemmed | A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation |
| title_short | A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation |
| title_sort | novel adaptive sparrow search algorithm based on chaotic mapping and t distribution mutation |
| topic | sparrow search algorithm chaotic mapping adaptive weight t-distribution mutations multi-strategy global optimization |
| url | https://www.mdpi.com/2076-3417/11/23/11192 |
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