Non-parametric particle swarm optimization for global optimization
In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several...
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| Format: | Article |
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Elsevier Ltd.
2015
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| _version_ | 1796860551146504192 |
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| author | Beheshti, Zahra Shamsuddin, Siti Mariyam |
| author_facet | Beheshti, Zahra Shamsuddin, Siti Mariyam |
| author_sort | Beheshti, Zahra |
| collection | ePrints |
| description | In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability. |
| first_indexed | 2024-03-05T19:43:03Z |
| format | Article |
| id | utm.eprints-58652 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:43:03Z |
| publishDate | 2015 |
| publisher | Elsevier Ltd. |
| record_format | dspace |
| spelling | utm.eprints-586522021-12-14T00:14:03Z http://eprints.utm.my/58652/ Non-parametric particle swarm optimization for global optimization Beheshti, Zahra Shamsuddin, Siti Mariyam QA75 Electronic computers. Computer science In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability. Elsevier Ltd. 2015 Article PeerReviewed Beheshti, Zahra and Shamsuddin, Siti Mariyam (2015) Non-parametric particle swarm optimization for global optimization. Applied Soft Computing Journal, 28 . pp. 345-359. ISSN 1568-4946 http://dx.doi.org/10.1016/j.asoc.2014.12.015 DOI:10.1016/j.asoc.2014.12.015 |
| spellingShingle | QA75 Electronic computers. Computer science Beheshti, Zahra Shamsuddin, Siti Mariyam Non-parametric particle swarm optimization for global optimization |
| title | Non-parametric particle swarm optimization for global optimization |
| title_full | Non-parametric particle swarm optimization for global optimization |
| title_fullStr | Non-parametric particle swarm optimization for global optimization |
| title_full_unstemmed | Non-parametric particle swarm optimization for global optimization |
| title_short | Non-parametric particle swarm optimization for global optimization |
| title_sort | non parametric particle swarm optimization for global optimization |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT beheshtizahra nonparametricparticleswarmoptimizationforglobaloptimization AT shamsuddinsitimariyam nonparametricparticleswarmoptimizationforglobaloptimization |