An adaptive chaos embedded particle swarm optimization algorithm

Chaos particle swarm optimization (CPSO) can not guarantee the population multiplicity and the optimized ergodicity, because its algorithm parameters are still random numbers in form. This paper proposes a new adaptive chaos embedded particle swarm optimization (ACEPSO) algorithm that uses chaotic maps to substitute random numbers of the classical PSO algorithm so as to make use of the properties of stochastic and ergodicity in chaotic search and introduces an adaptive inertia weight factor for each particle to adjust its inertia weight factor adaptively in response to its fitness, which can overcome the drawbacks of CPSO algorithm that is easily trapped in local optima. The experiments with complex and Multi-dimensional functions demonstrate that ACEPSO outperforms the original CPSO in the global searching ability and convergence rate.