PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been used to solve a variety of complex optimization problems. In spite of the acceptance of the algorithm in various fields, PSO still suffers from common issues such as premature convergence and local minima. This provides a...
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Faculty of Computer Science and Information Technology, University of Malaya
2018
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| author | Dada, Emmanuel Gbenga Ramlan, Effirul Ikhwan |
| author_facet | Dada, Emmanuel Gbenga Ramlan, Effirul Ikhwan |
| author_sort | Dada, Emmanuel Gbenga |
| collection | UM |
| description | Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been used to solve a variety of complex optimization problems. In spite of the acceptance of the algorithm in various fields, PSO still suffers from common issues such as premature convergence and local minima. This provides a platform for generating a variety of PSO variants. Although these variants are successful in addressing issues specific to a directed domain, they are still unable to resolve the issues effectively. The Interior-Point Methods (IPMs) are efficient tools for solving nonlinear optimization problems. On the one hand, the method is depicted as the most robust algorithm for solving large scale nonlinear optimization problems. On the other, similar to PSO, the methods are still plagued with several issues. We propose Primal-Dual Interior Point Particle Swarm Optimization (pdPSO) to resolve the shortcomings of a standard PSO without the limitations of the IPM methods. We applied the Primal Dual procedure to each particle in a finite number of iterations, and fed the PSO with the its output. We compared the performance of our new algorithm (pdPSO) with IPM and PSO using nine different dynamic benchmark functions. Our results revealed that pdPSO performed better than both the independent PSO algorithm and the IPM method. The proposed algorithm is not susceptible to premature convergence, and can better avoid local minima than conventional PSO, hence hypothetically it has the potential to perform better than many variants of PSO. |
| first_indexed | 2024-03-06T05:56:11Z |
| format | Article |
| id | um.eprints-22178 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:56:11Z |
| publishDate | 2018 |
| publisher | Faculty of Computer Science and Information Technology, University of Malaya |
| record_format | dspace |
| spelling | um.eprints-221782019-09-03T05:29:41Z http://eprints.um.edu.my/22178/ PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm Dada, Emmanuel Gbenga Ramlan, Effirul Ikhwan QA75 Electronic computers. Computer science Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been used to solve a variety of complex optimization problems. In spite of the acceptance of the algorithm in various fields, PSO still suffers from common issues such as premature convergence and local minima. This provides a platform for generating a variety of PSO variants. Although these variants are successful in addressing issues specific to a directed domain, they are still unable to resolve the issues effectively. The Interior-Point Methods (IPMs) are efficient tools for solving nonlinear optimization problems. On the one hand, the method is depicted as the most robust algorithm for solving large scale nonlinear optimization problems. On the other, similar to PSO, the methods are still plagued with several issues. We propose Primal-Dual Interior Point Particle Swarm Optimization (pdPSO) to resolve the shortcomings of a standard PSO without the limitations of the IPM methods. We applied the Primal Dual procedure to each particle in a finite number of iterations, and fed the PSO with the its output. We compared the performance of our new algorithm (pdPSO) with IPM and PSO using nine different dynamic benchmark functions. Our results revealed that pdPSO performed better than both the independent PSO algorithm and the IPM method. The proposed algorithm is not susceptible to premature convergence, and can better avoid local minima than conventional PSO, hence hypothetically it has the potential to perform better than many variants of PSO. Faculty of Computer Science and Information Technology, University of Malaya 2018 Article PeerReviewed Dada, Emmanuel Gbenga and Ramlan, Effirul Ikhwan (2018) PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm. Malaysian Journal of Computer Science, 31 (1). pp. 17-34. ISSN 0127-9084, DOI https://doi.org/10.22452/mjcs.vol31no1.2 <https://doi.org/10.22452/mjcs.vol31no1.2>. https://doi.org/10.22452/mjcs.vol31no1.2 doi:10.22452/mjcs.vol31no1.2 |
| spellingShingle | QA75 Electronic computers. Computer science Dada, Emmanuel Gbenga Ramlan, Effirul Ikhwan PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm |
| title | PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm |
| title_full | PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm |
| title_fullStr | PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm |
| title_full_unstemmed | PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm |
| title_short | PdPSO: The fusion of primal-dual interior point method and Particle Swarm Optimization algorithm |
| title_sort | pdpso the fusion of primal dual interior point method and particle swarm optimization algorithm |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT dadaemmanuelgbenga pdpsothefusionofprimaldualinteriorpointmethodandparticleswarmoptimizationalgorithm AT ramlaneffirulikhwan pdpsothefusionofprimaldualinteriorpointmethodandparticleswarmoptimizationalgorithm |