A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

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

Energies, 2021, vol. 14, issue 24, 1-19

Abstract: Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

Keywords: inverse problem; energy storage device; global optimization; PSO; adaptive mutation operator (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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