Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization
- DOI
- 10.2991/ijcis.d.210625.001How to use a DOI?
- Keywords
- Particle swarm optimization; Premature convergence; Optimization performance improvement; Reliability optimization
- Abstract
Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Bo Zheng AU - Xin Ma AU - Xiaoqiang Zhang AU - Huiying Gao PY - 2021 DA - 2021/07/01 TI - Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization JO - International Journal of Computational Intelligence Systems SP - 1842 EP - 1855 VL - 14 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.210625.001 DO - 10.2991/ijcis.d.210625.001 ID - Zheng2021 ER -