Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs
To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the populat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/8/576 |
| _version_ | 1797381879410720768 |
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| author | Junjie Tang Lianguo Wang |
| author_facet | Junjie Tang Lianguo Wang |
| author_sort | Junjie Tang |
| collection | DOAJ |
| description | To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments. |
| first_indexed | 2024-03-08T20:57:53Z |
| format | Article |
| id | doaj.art-97848d2bffaa43b6a776b77c2219e08a |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T20:57:53Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-97848d2bffaa43b6a776b77c2219e08a2023-12-22T13:55:31ZengMDPI AGBiomimetics2313-76732023-12-018857610.3390/biomimetics8080576Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization DesignsJunjie Tang0Lianguo Wang1College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, ChinaTo address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments.https://www.mdpi.com/2313-7673/8/8/576swarm intelligencesine cosine algorithmtent chaotic mappingnonlinearityelite individualoptimization problem |
| spellingShingle | Junjie Tang Lianguo Wang Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs Biomimetics swarm intelligence sine cosine algorithm tent chaotic mapping nonlinearity elite individual optimization problem |
| title | Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs |
| title_full | Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs |
| title_fullStr | Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs |
| title_full_unstemmed | Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs |
| title_short | Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs |
| title_sort | sine cosine algorithm for elite individual collaborative search and its application in mechanical optimization designs |
| topic | swarm intelligence sine cosine algorithm tent chaotic mapping nonlinearity elite individual optimization problem |
| url | https://www.mdpi.com/2313-7673/8/8/576 |
| work_keys_str_mv | AT junjietang sinecosinealgorithmforeliteindividualcollaborativesearchanditsapplicationinmechanicaloptimizationdesigns AT lianguowang sinecosinealgorithmforeliteindividualcollaborativesearchanditsapplicationinmechanicaloptimizationdesigns |