Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight
Particle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimension...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2018-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/311122 |
| _version_ | 1827282740215545856 |
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| author | Shafi Ullah Khan Obaid Ur Rehman Naeem Khan Asfandyar Khan Syed Anayat Ali Shah Shiyou Yang |
| author_facet | Shafi Ullah Khan Obaid Ur Rehman Naeem Khan Asfandyar Khan Syed Anayat Ali Shah Shiyou Yang |
| author_sort | Shafi Ullah Khan |
| collection | DOAJ |
| description | Particle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimensional optimization problems. To handle the problem of premutature convergence in PSO, this paper presents a novel adaptive inertia weight strategy and modifies the velocity update equation with the new Sbest term. To maintain the diversity of the population a particular radius r is introduced to impulse cluster particles. To validate the effectiveness of the proposed algorithm, various test functions and typical engineering applications are employed, and the experimental results show that with the changing of the proposed parameter the performance of PSO improves when dealing with these complex and high dimensional problems. |
| first_indexed | 2024-04-24T09:24:21Z |
| format | Article |
| id | doaj.art-21ae66e34ab84c8a9cda84b3f9d3a7f2 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:24:21Z |
| publishDate | 2018-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-21ae66e34ab84c8a9cda84b3f9d3a7f22024-04-15T15:12:06ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392018-01-012561631163710.17559/TV-20170413135109Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia WeightShafi Ullah Khan0Obaid Ur Rehman1Naeem Khan2Asfandyar Khan3Syed Anayat Ali Shah4Shiyou Yang5Department of Electronics Islamia College University, Peshawar, PakistanSarhad University of Science & Information Technology, Peshawar, PakistanDepertment of Electrical Engineering, University of Engineering & Technology, Bannu Campus, PakistanDepartment of CS/IT University of Agriculture, Peshawar, PakistanDepartment of Mathimatics, Islamia College University, Peshawar, PakistanCollege Electrical Engineering, Zhejiang University, Hangzhou, ChinaParticle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimensional optimization problems. To handle the problem of premutature convergence in PSO, this paper presents a novel adaptive inertia weight strategy and modifies the velocity update equation with the new Sbest term. To maintain the diversity of the population a particular radius r is introduced to impulse cluster particles. To validate the effectiveness of the proposed algorithm, various test functions and typical engineering applications are employed, and the experimental results show that with the changing of the proposed parameter the performance of PSO improves when dealing with these complex and high dimensional problems.https://hrcak.srce.hr/file/311122adaptive inertia weightglobal optimizationPSOradius r, S best particleTeam 22 |
| spellingShingle | Shafi Ullah Khan Obaid Ur Rehman Naeem Khan Asfandyar Khan Syed Anayat Ali Shah Shiyou Yang Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight Tehnički Vjesnik adaptive inertia weight global optimization PSO radius r, S best particle Team 22 |
| title | Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight |
| title_full | Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight |
| title_fullStr | Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight |
| title_full_unstemmed | Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight |
| title_short | Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight |
| title_sort | improving the diversity of pso for an engineering inverse problem using adaptive inertia weight |
| topic | adaptive inertia weight global optimization PSO radius r, S best particle Team 22 |
| url | https://hrcak.srce.hr/file/311122 |
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