Dongping Tian1,*, Bingchun Li1, Jing Liu1, Chen Liu1, Ling Yuan1, Zhongzhi Shi2
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2783-2807, 2023, DOI:10.32604/iasc.2023.039531
Abstract Particle swarm optimization (PSO) is a stochastic computation technique that has become an increasingly important branch of swarm intelligence optimization. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems. Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization (abbreviated as AMS-PSO). To start with, the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO. Subsequently, according to the current iteration, different update schemes are used to regulate the particle search process at different evolution stages.… More >
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