A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices
Particle swarm optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the natural behavior of birds flocking or fish schooling. The PSO’s main advantages are its ease of implementation and a small number of fine-tuning parameters. However, the major drawbacks...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9476009/ |
id |
doaj-b6dedffb3f79489a96ffe2f3590dbb73 |
---|---|
record_format |
Article |
spelling |
doaj-b6dedffb3f79489a96ffe2f3590dbb732021-07-20T23:00:27ZengIEEEIEEE Access2169-35362021-01-019999329994310.1109/ACCESS.2021.30954039476009A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic DevicesRehan Ali Khan0https://orcid.org/0000-0001-8917-9278Shiyou Yang1https://orcid.org/0000-0002-8933-7034Shah Fahad2Shafi Ullah Khan3https://orcid.org/0000-0001-7958-8422 Kalimullah4College of Electrical Engineering, Zhejiang University, Hangzhou, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou, ChinaDepartment of Electronics, Islamia College University Peshawar, Peshawar, PakistanCollege of Electrical Engineering, Zhejiang University, Hangzhou, ChinaParticle swarm optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the natural behavior of birds flocking or fish schooling. The PSO’s main advantages are its ease of implementation and a small number of fine-tuning parameters. However, the major drawbacks of an existing PSO are its premature convergence and the lack of a balance of exploration and exploitation searches in the search space. To address the aforementioned problems, a new concept known as a smart particle swarm optimization (SPSO) process is introduced and implemented. The smart particle that leads the swarm in the proposed concept has eidetic memory behavior. The smart particle mainly works under the principles of a convergence factor (CF) technique, which integrates the memorization of particles positions, comparison, and leader declaration for the best optimal solution. Additionally, CF uses a particle position vector instead of a particle fitness or mutation to increase the exploration capability in the search space. The TEAM Workshop Problem 22, a super conducting magnetic energy storage (SMES) system; and some well-known benchmark optimization test functions are numerically solved to verify the efficacy of the proposed SPSO. The SPSO finds a better optimal solution than the other tested algorithms, particularly in the initial computational evaluation of the generation according to numerical experiments and case study analysis.https://ieeexplore.ieee.org/document/9476009/Smart particleposition vectors of particleselectromagnetic deviceparticle swarm optimizationglobal optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rehan Ali Khan Shiyou Yang Shah Fahad Shafi Ullah Khan Kalimullah |
spellingShingle |
Rehan Ali Khan Shiyou Yang Shah Fahad Shafi Ullah Khan Kalimullah A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices IEEE Access Smart particle position vectors of particles electromagnetic device particle swarm optimization global optimization |
author_facet |
Rehan Ali Khan Shiyou Yang Shah Fahad Shafi Ullah Khan Kalimullah |
author_sort |
Rehan Ali Khan |
title |
A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices |
title_short |
A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices |
title_full |
A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices |
title_fullStr |
A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices |
title_full_unstemmed |
A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices |
title_sort |
modified particle swarm optimization with a smart particle for inverse problems in electromagnetic devices |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Particle swarm optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the natural behavior of birds flocking or fish schooling. The PSO’s main advantages are its ease of implementation and a small number of fine-tuning parameters. However, the major drawbacks of an existing PSO are its premature convergence and the lack of a balance of exploration and exploitation searches in the search space. To address the aforementioned problems, a new concept known as a smart particle swarm optimization (SPSO) process is introduced and implemented. The smart particle that leads the swarm in the proposed concept has eidetic memory behavior. The smart particle mainly works under the principles of a convergence factor (CF) technique, which integrates the memorization of particles positions, comparison, and leader declaration for the best optimal solution. Additionally, CF uses a particle position vector instead of a particle fitness or mutation to increase the exploration capability in the search space. The TEAM Workshop Problem 22, a super conducting magnetic energy storage (SMES) system; and some well-known benchmark optimization test functions are numerically solved to verify the efficacy of the proposed SPSO. The SPSO finds a better optimal solution than the other tested algorithms, particularly in the initial computational evaluation of the generation according to numerical experiments and case study analysis. |
topic |
Smart particle position vectors of particles electromagnetic device particle swarm optimization global optimization |
url |
https://ieeexplore.ieee.org/document/9476009/ |
work_keys_str_mv |
AT rehanalikhan amodifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT shiyouyang amodifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT shahfahad amodifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT shafiullahkhan amodifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT kalimullah amodifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT rehanalikhan modifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT shiyouyang modifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT shahfahad modifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT shafiullahkhan modifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices AT kalimullah modifiedparticleswarmoptimizationwithasmartparticleforinverseproblemsinelectromagneticdevices |
_version_ |
1721293276449865728 |