A novel grouping PSO algorithm for solving multi-modal high-dimensional functions

A novel grouping Particle Swarm Optimization (PSO) algorithm(for short GPSO) is proposed in this paper to solve the problem of premature convergence of PSO algorithm for multi-mode and high-dimension functions. In this algorithm, the solution space of an optimization problem is divided into Q subspaces. N particles are assigned to each subspace, and a total number of Q particle swarms search independently in their own space. In order to strengthen the search diversity and ergodicity of each particle, the location of particles in each group are initialized by chaotic sequence. Our numerical experiments have demonstrated that the algorithm has very fast convergence speed, and impressive performance in the optimization applications in the multi-modal and high-dimensional functions.