Adaptive particle swarm optimization with multi-dimensional mutation

Abstract

The paper presents adaptive particle swarm optimization with multi-dimensional mutation (MM-APSO), which can perform move efficient search than the conventional adaptive particle swarm optimization (APSO). In particular, it can solve non-separable fitness functions such as banana functions with high accuracy and rapid convergence. MM-APSO consists of APSO and additional two methods. One is multi-dimensional mutation, which uses movement vector of population. The other is reinitializing velocity to 0 when mutation occurs. Experiments were conducted on 10 unimodal and multimodal benchmark functions. The experimental results show that MM-APSO substantially enhances the performance of the APSO in terms of convergence speed and solution accuracy.