Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms
In this paper, the performance of segment particle swarm optimization (Se-PSO) algorithm was compared with that of original particle swarm optimization (PSO) algorithm. Four different benchmark functions of Sphere, Rosenbrock, Rastrigin, and Griewank with asymmetric initial range settings (upper and...
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Online Access: | http://umpir.ump.edu.my/id/eprint/26313/1/Empirical%20Study%20of%20Segment%20Particle.pdf http://umpir.ump.edu.my/id/eprint/26313/ https://dx.doi.org/10.14569/IJACSA.2019.0100862 https://dx.doi.org/10.14569/IJACSA.2019.0100862 |
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my.ump.umpir.263132019-11-04T05:36:02Z http://umpir.ump.edu.my/id/eprint/26313/ Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir QA Mathematics In this paper, the performance of segment particle swarm optimization (Se-PSO) algorithm was compared with that of original particle swarm optimization (PSO) algorithm. Four different benchmark functions of Sphere, Rosenbrock, Rastrigin, and Griewank with asymmetric initial range settings (upper and lower boundaries values) were selected as the test functions. The experimental results showed that, the Se-PSO algorithm achieved better results in terms of faster convergences in all the testing cases compared to the original PSO algorithm. However, the experimental results further showed the Se-PSO as a promising optimization algorithm method in some other different fields. The Science and Information (SAI) Organization Limited 2019 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/26313/1/Empirical%20Study%20of%20Segment%20Particle.pdf Azrag, M. A. K. and Tuty Asmawaty, Abdul Kadir (2019) Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms. International Journal of Advanced Computer Science and Applications (IJACSA), 10 (8). pp. 480-485. ISSN 2156-5570(Online) https://dx.doi.org/10.14569/IJACSA.2019.0100862 https://dx.doi.org/10.14569/IJACSA.2019.0100862 |
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QA Mathematics Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms |
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In this paper, the performance of segment particle swarm optimization (Se-PSO) algorithm was compared with that of original particle swarm optimization (PSO) algorithm. Four different benchmark functions of Sphere, Rosenbrock, Rastrigin, and Griewank with asymmetric initial range settings (upper and lower boundaries values) were selected as the test functions. The experimental results showed that, the Se-PSO algorithm achieved better results in terms of faster convergences in all the testing cases compared to the original PSO algorithm. However, the experimental results further showed the Se-PSO as a promising optimization algorithm method in some other different fields. |
format |
Article |
author |
Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir |
author_facet |
Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir |
author_sort |
Azrag, M. A. K. |
title |
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms |
title_short |
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms |
title_full |
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms |
title_fullStr |
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms |
title_full_unstemmed |
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms |
title_sort |
empirical study of segment particle swarm optimization and particle swarm optimization algorithms |
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The Science and Information (SAI) Organization Limited |
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2019 |
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http://umpir.ump.edu.my/id/eprint/26313/1/Empirical%20Study%20of%20Segment%20Particle.pdf http://umpir.ump.edu.my/id/eprint/26313/ https://dx.doi.org/10.14569/IJACSA.2019.0100862 https://dx.doi.org/10.14569/IJACSA.2019.0100862 |
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