Abstract
In this paper, a novel fuzzy particle swarm optimization (NFPSO), in which inertia weight as well as the learning coefficient can be adaptively adjusted according to the control information translated from the fuzzy logic controller (FLC) during the search process, is presented by introducing a two-input and two-output FLC into the canonical particle swarm optimization (CPSO). The effectiveness of NFPSO proposed in this paper is demonstrated by applying it to three benchmark functions obtained from the literature. The simulation results show that NFPSO outperforms CPSO and other fuzzy PSO versions.
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