A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Shah Fahad, Shiyou Yang, Rehan Ali Khan, Shafiullah Khan and Shoaib Ahmed Khan
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Shah Fahad: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Shiyou Yang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Rehan Ali Khan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Shafiullah Khan: Department of Electronics, Islamia College University, Peshawar 25000, Pakistan
Shoaib Ahmed Khan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2021, vol. 14, issue 15, 1-11

Abstract: Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

Keywords: smart quantum particle; particle swarm optimization; design optimization; electromagnetic problem (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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