Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization
Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/12/2280 |
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| author | Nafees Ul Hassan Waqas Haider Bangyal M. Sadiq Ali Khan Kashif Nisar Ag. Asri Ag. Ibrahim Danda B. Rawat |
| author_facet | Nafees Ul Hassan Waqas Haider Bangyal M. Sadiq Ali Khan Kashif Nisar Ag. Asri Ag. Ibrahim Danda B. Rawat |
| author_sort | Nafees Ul Hassan |
| collection | DOAJ |
| description | Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence. |
| first_indexed | 2024-03-10T03:00:34Z |
| format | Article |
| id | doaj.art-09dc0ddc8ae74b428848aa7aacf29b17 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T03:00:34Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
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| series | Symmetry |
| spelling | doaj.art-09dc0ddc8ae74b428848aa7aacf29b172023-11-23T10:44:47ZengMDPI AGSymmetry2073-89942021-12-011312228010.3390/sym13122280Improved Opposition-Based Particle Swarm Optimization Algorithm for Global OptimizationNafees Ul Hassan0Waqas Haider Bangyal1M. Sadiq Ali Khan2Kashif Nisar3Ag. Asri Ag. Ibrahim4Danda B. Rawat5Department of Computer Science, University of Gujrat, Gujrat 50700, PakistanDepartment of Computer Science, University of Gujrat, Gujrat 50700, PakistanDepartment of Computer Science, University of Karachi, Karachi 75270, PakistanFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, MalaysiaData Science and Cybersecurity Center, Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USAParticle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence.https://www.mdpi.com/2073-8994/13/12/2280pseudo-random sequencesPhiloxThreefrydata classificationANN |
| spellingShingle | Nafees Ul Hassan Waqas Haider Bangyal M. Sadiq Ali Khan Kashif Nisar Ag. Asri Ag. Ibrahim Danda B. Rawat Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization Symmetry pseudo-random sequences Philox Threefry data classification ANN |
| title | Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization |
| title_full | Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization |
| title_fullStr | Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization |
| title_full_unstemmed | Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization |
| title_short | Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization |
| title_sort | improved opposition based particle swarm optimization algorithm for global optimization |
| topic | pseudo-random sequences Philox Threefry data classification ANN |
| url | https://www.mdpi.com/2073-8994/13/12/2280 |
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