An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization
Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the d...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6084917 |
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| author | Yetao Ji Jiaze Tu Hanfeng Zhou Wenyong Gui Guoxi Liang Huiling Chen Mingjing Wang |
| author_facet | Yetao Ji Jiaze Tu Hanfeng Zhou Wenyong Gui Guoxi Liang Huiling Chen Mingjing Wang |
| author_sort | Yetao Ji |
| collection | DOAJ |
| description | Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems. |
| format | Article |
| id | doaj-art-3d66f499c38b44f9bf920c8aba390fa4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3d66f499c38b44f9bf920c8aba390fa42025-08-20T03:35:51ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/60849176084917An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained OptimizationYetao Ji0Jiaze Tu1Hanfeng Zhou2Wenyong Gui3Guoxi Liang4Huiling Chen5Mingjing Wang6College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaDepartment of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamSine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems.http://dx.doi.org/10.1155/2020/6084917 |
| spellingShingle | Yetao Ji Jiaze Tu Hanfeng Zhou Wenyong Gui Guoxi Liang Huiling Chen Mingjing Wang An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization Complexity |
| title | An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization |
| title_full | An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization |
| title_fullStr | An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization |
| title_full_unstemmed | An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization |
| title_short | An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization |
| title_sort | adaptive chaotic sine cosine algorithm for constrained and unconstrained optimization |
| url | http://dx.doi.org/10.1155/2020/6084917 |
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