Clustering Quantum-Behaved Particle Swarm Optimization Algorithm for Solving Dynamic Optimization Problems

Abstract

Solving dynamic optimization problems (DOPs) has become the research focus in the optimization area in recent years. In view of the dynamics and complexity of DOPs, quantum-behaved particle swarm optimization (QPSO) algorithm, which is based on the quantum mechanics and Particle Swarm Optimization (PSO) algorithm, is proposed in this paper to solve DOPs with the help of the algorithms global search ability. The hierarchical clustering method is also used in the QPSO algorithm in order to enhance the relocation ability and improve the ability of tracking the optimal solution. During the optimization procedure, the convergence check, overcrowding check, and over-lapping check are appointed to keep the diversity of the swarm. Experimental results on the standard benchmark functions show that QPSO algorithm with hierarchical clustering and diversity maintaining has strong ability to adapt the dynamics and good optimization ability.