Study on the particle swarm optimization based on double mutation

Abstract

An improved particle swarm optimization based on double mutation is proposed in this paper. The algorithm adopts nonlinear dynamic adjustment of inertia weight and adaptive double mutation mechanism, which makes the algorithm conducive to balancing the development and detecting ability of particles and the algorithm can effectively jump out of local minima. In order to verify the validity of the proposed algorithm, a set of representative Benchmark test functions are used to test the algorithm.