LAPSE:2023.25564
Published Article
LAPSE:2023.25564
A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems
Shah Fahad, Shiyou Yang, Rehan Ali Khan, Shafiullah Khan, Shoaib Ahmed Khan
March 28, 2023
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
design optimization, electromagnetic problem, Particle Swarm Optimization, smart quantum particle
Suggested Citation
Fahad S, Yang S, Khan RA, Khan S, Khan SA. A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems. (2023). LAPSE:2023.25564
Author Affiliations
Fahad S: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China [ORCID]
Yang S: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Khan RA: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China [ORCID]
Khan S: Department of Electronics, Islamia College University, Peshawar 25000, Pakistan
Khan SA: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Journal Name
Energies
Volume
14
Issue
15
First Page
4613
Year
2021
Publication Date
2021-07-30
Published Version
ISSN
1996-1073
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PII: en14154613, Publication Type: Journal Article
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LAPSE:2023.25564
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doi:10.3390/en14154613
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