An Improved Particle Swarm Optimization Based on Diversity Monitor and Real-time Updating Strategy

Abstract

Abstract: Particle swarm optimization (PSO) algorithm has strong ability to explore global optimal region for heat exchanger networks synthesis. However, particles may trap into local optima and converge prematurely in late evolution. Therefore, an improved particle swarm optimization algorithm based on diversity feedback and real-time updating strategy is proposed. Firstly, index of population health degree is established to evaluate population diversity during evolution. Secondly, a random perturbation strategy and a centrifugal strategy are combined respectively with PSO algorithm to enrich population diversity and enhance global search ability. Furthermore, gradient search strategy is applied to search efficiently local optima and improve computational efficiency of PSO algorithm. Finally, a feedback mechanism of population health degree is proposed to real-time monitor health status of population and further to adopt different update strategies for keeping particles healthy during evolution. The method was applied to several cases taken from literature and results are encouraging. They are better than those of other improvements for PSO.

Key words: heat exchanger networks, particle swarm optimization, population diversity, local search

CLC Number:

TK124

LI Shuailong, CUI Guomin, CHEN Jiaxing, XIAO Yuan. An Improved Particle Swarm Optimization Based on Diversity Monitor and Real-time Updating Strategy[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2017, 34(3): 344-354.

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