A radius-free quantum particle swarm optimization technique for dynamic optimization problems

Abstract

The quantum particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO) algorithm aimed at solving dynamic optimization problems. Some particles in the QPSO algorithm are selected as “quantum” particles and the positions of these particles are sampled, using some probability distribution, within a radius (i.e., a hypersphere) around the global best position while the remainder of particles follow standard PSO behaviour. The exploration and exploitation of the QPSO algorithm is heavily influenced by the probability distribution used as well as the size of the quantum radius. However, the best probability distribution and radius size are both problem and environment dependent. This work proposes using a parent centric crossover (PCX) operator to generate the positions of quantum particles, thereby removing the need for radius and probability distribution parameters completely. Two variants are proposed and results indicate that both variants are superior to QPSO, especially in environments exhibiting high temporal severity.