Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization
Slime Mould Algorithm (SMA) is a new meta-heuristics algorithm that is inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown its competitive performance among other meta-heuristics algorithms and has been used in many mathematical optimization and real...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9797686/ |
| _version_ | 1818219272929804288 |
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| author | Hui Chen Xiaobo Li Shaolang Li Yuxin Zhao Junwei Dong |
| author_facet | Hui Chen Xiaobo Li Shaolang Li Yuxin Zhao Junwei Dong |
| author_sort | Hui Chen |
| collection | DOAJ |
| description | Slime Mould Algorithm (SMA) is a new meta-heuristics algorithm that is inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown its competitive performance among other meta-heuristics algorithms and has been used in many mathematical optimization and real-world problems. However, SMA tends to sink into local optimality and lacks the diversity of the population. Therefore, to cope with the drawbacks of the classical SMA, this paper proposes an improved SMA algorithm named CHDESMA. First of all, the chaotic maps methods have the characteristics of ergodicity and randomness, and we used chaotic maps methods to replace the original random initialization to improve the diversity of the algorithm, which is more suitable for exploring the potential areas in the early stage. Then, based on the superior searching ability of the differential evolution algorithm (DE), the crossover and selection operations of DE are applied to CHDESMA, and the position is updated by the combination of the original SMA operator and the mutation strategy of DE, which effectively avoids the algorithm falling into local optimum. CHDESMA was evaluated using CEC2014 and CEC2017 test suits and four real-world engineering problems. CHDESMA was compared with advanced algorithms and DE variants. The experimental results and statistical analysis indicate that CHDESMA has competitive performance compared with the state-of-the-art algorithms. |
| first_indexed | 2024-12-12T07:37:02Z |
| format | Article |
| id | doaj.art-0d475be9019a4969b2f0e407e5e55bfd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T07:37:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0d475be9019a4969b2f0e407e5e55bfd2022-12-22T00:32:53ZengIEEEIEEE Access2169-35362022-01-0110668116683010.1109/ACCESS.2022.31836279797686Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global OptimizationHui Chen0https://orcid.org/0000-0003-2302-2416Xiaobo Li1https://orcid.org/0000-0003-0607-5567Shaolang Li2https://orcid.org/0000-0002-2072-3148Yuxin Zhao3https://orcid.org/0000-0002-7649-1788Junwei Dong4https://orcid.org/0000-0001-6597-0874College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, ChinaCollege of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, ChinaCollege of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, ChinaCollege of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, ChinaCollege of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, ChinaSlime Mould Algorithm (SMA) is a new meta-heuristics algorithm that is inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown its competitive performance among other meta-heuristics algorithms and has been used in many mathematical optimization and real-world problems. However, SMA tends to sink into local optimality and lacks the diversity of the population. Therefore, to cope with the drawbacks of the classical SMA, this paper proposes an improved SMA algorithm named CHDESMA. First of all, the chaotic maps methods have the characteristics of ergodicity and randomness, and we used chaotic maps methods to replace the original random initialization to improve the diversity of the algorithm, which is more suitable for exploring the potential areas in the early stage. Then, based on the superior searching ability of the differential evolution algorithm (DE), the crossover and selection operations of DE are applied to CHDESMA, and the position is updated by the combination of the original SMA operator and the mutation strategy of DE, which effectively avoids the algorithm falling into local optimum. CHDESMA was evaluated using CEC2014 and CEC2017 test suits and four real-world engineering problems. CHDESMA was compared with advanced algorithms and DE variants. The experimental results and statistical analysis indicate that CHDESMA has competitive performance compared with the state-of-the-art algorithms.https://ieeexplore.ieee.org/document/9797686/Slime mould algorithmdifferential evolutionchaotic mapsfunction optimizationengineering design problem |
| spellingShingle | Hui Chen Xiaobo Li Shaolang Li Yuxin Zhao Junwei Dong Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization IEEE Access Slime mould algorithm differential evolution chaotic maps function optimization engineering design problem |
| title | Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization |
| title_full | Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization |
| title_fullStr | Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization |
| title_full_unstemmed | Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization |
| title_short | Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization |
| title_sort | improved slime mould algorithm hybridizing chaotic maps and differential evolution strategy for global optimization |
| topic | Slime mould algorithm differential evolution chaotic maps function optimization engineering design problem |
| url | https://ieeexplore.ieee.org/document/9797686/ |
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