Team Collaboration Particle Swarm Optimization and Its Application on 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.

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

“…In order to verify the global optimization ability of the DTPSO proposed in this paper, it will be compared with other swarm intelligence algorithms. A team collaboration PSO (TCPSO) inspired by human team collaboration behavior, 37 a self‐regulating PSO imitating human cognitive psychology, 38 a dynamic neighborhood PSO (DPSO) designed by neighborhood topology, 39 and a multiple agents PSO (MAPSO) integrating with agent technology 40 will compare with DTPSO, these PSO variants have excellent global optimization abilities in dealing with complex and high‐dimensional optimization problems. The basic settings of the PSO variants are as same as those in Section 3.1, for some specific parameter settings, please refer to relevant references listed above.…”

Optimization of polydopamine coating process for poly lactic acid‐based 3D printed bone plates using machine learning approaches

“…It is capable of addressing high dimensional problems that involve variables and constraints as it enables the generated solutions to conform to the constraints imposed by the optimization problem. [54][55][56] Unfortunately, it also has some disadvantages: algorithm solely relies on the swarm optima and individual optima. 57 (3) It exhibits stochastic behavior, whereby the solutions produced may exhibit significant variability across multiple iterations.…”

A hybrid feature selection model for identifying groups of critical elements in aero‐engine assembly

“…Filter methods may ignore the interrelationships between features, while wrapper methods may lead to high computational costs and are prone to overfitting. 25 Given this, a combined strategy using two-stage feature selection can provide more accurate classification results. [26][27][28][29] Based on existing research, although there have been attempts to extract key information from complex systems, there is still a lack of an effective framework to systematically compare different methods.…”

“…However, both approaches have their shortcomings. Filter methods may ignore the interrelationships between features, while wrapper methods may lead to high computational costs and are prone to overfitting 25 . Given this, a combined strategy using two‐stage feature selection can provide more accurate classification results 26–29 …”