Affiliations: [a] Department of Electrical Engineering, Sarhad University of Science & IT, Peshawar, Pakistan | [b] Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China | [c] Department of Electronics, Islamia College University, Peshawar, Kpk, 25000, Pakistan | [d] College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China
Correspondence:
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Corresponding author: Shanshan Tu, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China. E-mail: [email protected]
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.
Keywords: Electromagnetic design problem, global optimization, mutation, quantum mechanics
