A modified quantum particle swarm optimizer applied to optimization design of electromagnetic devices

Abstract

Quantum particle swarm optimization (QPSO) is a swarm intelligence method that has been successfully applied to solve a wide scope of electromagnetic inverse problems. The method encounters into local optima and insufficient diversity at the later phase of optimization. To address this type of issue, a new methodology is used to select the fittest particle, and a novel mutation mechanism is introduced, in which a mutation technique is applied on the global best particle to avoid the population from assembling and facilitating the individual to avoid the local optimum easily. In addition, a parameter updating strategy is proposed, which facilitates the optimizer to maintain a good balance between local and global searches. To demonstrate the merit and efficiency of the proposed methodology, the evaluated results from the case studies are presented.