Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem
Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorit...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-02-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2688.pdf |
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| author | Qingrui Li Yongquan Zhou Qifang Luo |
| author_facet | Qingrui Li Yongquan Zhou Qifang Luo |
| author_sort | Qingrui Li |
| collection | DOAJ |
| description | Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two phases: first, independently handling each optimization problem; second, transferring knowledge. Knowledge transfer is determined by the probability of knowledge transfer and the selection probability of elite individuals. Based on this decision, the algorithm either transfers elite knowledge from other tasks or updates the current task through self-perturbation. Experimental results indicate that, compared to other advanced MTO algorithms, the proposed algorithm achieves the most accurate solutions on multitask benchmark functions, the five-task and 10-task planar kinematic arm control problems, the multitask robot gripper problem, and the multitask car side-impact design problem. The code and data for this article can be obtained from: https://doi.org/10.5281/zenodo.14197420. |
| format | Article |
| id | doaj-art-e1b47ae3871b4f45bb6d18edf08014c0 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e1b47ae3871b4f45bb6d18edf08014c02025-08-20T03:11:33ZengPeerJ Inc.PeerJ Computer Science2376-59922025-02-0111e268810.7717/peerj-cs.2688Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problemQingrui Li0Yongquan Zhou1Qifang Luo2College of Artificial Intelligence, Guangxi Minzu University, Nanning, Guangxi, ChinaCollege of Artificial Intelligence, Guangxi Minzu University, Nanning, Guangxi, ChinaCollege of Artificial Intelligence, Guangxi Minzu University, Nanning, Guangxi, ChinaMulti-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two phases: first, independently handling each optimization problem; second, transferring knowledge. Knowledge transfer is determined by the probability of knowledge transfer and the selection probability of elite individuals. Based on this decision, the algorithm either transfers elite knowledge from other tasks or updates the current task through self-perturbation. Experimental results indicate that, compared to other advanced MTO algorithms, the proposed algorithm achieves the most accurate solutions on multitask benchmark functions, the five-task and 10-task planar kinematic arm control problems, the multitask robot gripper problem, and the multitask car side-impact design problem. The code and data for this article can be obtained from: https://doi.org/10.5281/zenodo.14197420.https://peerj.com/articles/cs-2688.pdfMulti-task optimizationSnake optimizationMultitask snake optimization algorithmPlanar kinematic arm control problemIntelligence algorithm |
| spellingShingle | Qingrui Li Yongquan Zhou Qifang Luo Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem PeerJ Computer Science Multi-task optimization Snake optimization Multitask snake optimization algorithm Planar kinematic arm control problem Intelligence algorithm |
| title | Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem |
| title_full | Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem |
| title_fullStr | Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem |
| title_full_unstemmed | Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem |
| title_short | Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem |
| title_sort | multi task snake optimization algorithm for global optimization and planar kinematic arm control problem |
| topic | Multi-task optimization Snake optimization Multitask snake optimization algorithm Planar kinematic arm control problem Intelligence algorithm |
| url | https://peerj.com/articles/cs-2688.pdf |
| work_keys_str_mv | AT qingruili multitasksnakeoptimizationalgorithmforglobaloptimizationandplanarkinematicarmcontrolproblem AT yongquanzhou multitasksnakeoptimizationalgorithmforglobaloptimizationandplanarkinematicarmcontrolproblem AT qifangluo multitasksnakeoptimizationalgorithmforglobaloptimizationandplanarkinematicarmcontrolproblem |