Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance
Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is pr...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/5129098 |
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| author | Lei Wen Guopeng Wang Longwang Yue Xiaodan Liang Hanning Chen |
| author_facet | Lei Wen Guopeng Wang Longwang Yue Xiaodan Liang Hanning Chen |
| author_sort | Lei Wen |
| collection | DOAJ |
| description | Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population’s individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness. |
| format | Article |
| id | doaj-art-6adcf8d9b7284ef1846d1febf5717a56 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
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| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-6adcf8d9b7284ef1846d1febf5717a562025-02-03T05:50:38ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/5129098Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual GuidanceLei Wen0Guopeng Wang1Longwang Yue2Xiaodan Liang3Hanning Chen4School of Computer Science and TechnologyThe Open University of ChinaSchool of Mechanical and Electrical EngineeringSchool of Computer Science and TechnologySchool of Computer Science and TechnologyAiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population’s individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness.http://dx.doi.org/10.1155/2022/5129098 |
| spellingShingle | Lei Wen Guopeng Wang Longwang Yue Xiaodan Liang Hanning Chen Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance Discrete Dynamics in Nature and Society |
| title | Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance |
| title_full | Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance |
| title_fullStr | Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance |
| title_full_unstemmed | Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance |
| title_short | Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance |
| title_sort | multistrategy harris hawks optimization algorithm using chaotic method cauchy mutation and elite individual guidance |
| url | http://dx.doi.org/10.1155/2022/5129098 |
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