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A co-evolving framework for robust particle swarm optimization. (English) Zbl 1143.65046
Summary: Particle swarm optimization (PSO) as an efficient and powerful problem-solving strategy has been widely used, but appropriate adjustment of its parameters usually requires a lot of time and labor. So a co-evolving framework is proposed to improve the robustness of the PSO. Within this framework the fuzzy rules for the manipulation of the inertia weights are co-evolved with the particles. The simulation results on a suite of test functions show that the use of this co-evolving framework improves the performance of the PSO, especially the robustness against the dimensional variation of the test functions.

MSC:
65K05 Numerical mathematical programming methods
90C15 Stochastic programming
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