A Quantum Particle Swarm Optimization Method With Fitness Selection Methodology for Electromagnetic Inverse Problems

Abstract

The objective of the research is to extend the potential of the standard quantum particle swarm optimization (QPSO) method for electromagnetic inverse problems. As, QPSO trapped into local optima while dealing with complex design problems. In order to address this type of issue, to avoid from trapping into local optima and tradeoff between the exploration and exploitation searches, a novel methodology is employed which includes the design of a new position updating formula, the introduction of a novel fitness selection methodology, and the proposal of a dynamic parameter updating strategy. Nevertheless, the evaluated results as reported have revealed that the proposed modified quantum-inspired particle swarm optimization method for global optimization and electromagnetic inverse problems can find better outcomes at initial stage of the iterating process as compared with other tested optimal methods.