Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem
The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclus...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9072130/ |
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| author | Binh Minh Nguyen Trung Tran Thieu Nguyen Giang Nguyen |
| author_facet | Binh Minh Nguyen Trung Tran Thieu Nguyen Giang Nguyen |
| author_sort | Binh Minh Nguyen |
| collection | DOAJ |
| description | The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence. The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability. |
| format | Article |
| id | doaj-art-8ea2ff0855cb4175a52f3726cadb61d2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8ea2ff0855cb4175a52f3726cadb61d22024-11-29T00:01:06ZengIEEEIEEE Access2169-35362020-01-018749917501010.1109/ACCESS.2020.29887179072130Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search ProblemBinh Minh Nguyen0https://orcid.org/0000-0003-1328-3647Trung Tran1https://orcid.org/0000-0003-2386-7064Thieu Nguyen2https://orcid.org/0000-0001-9994-8747Giang Nguyen3https://orcid.org/0000-0002-6769-0195School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, VietnamSchool of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, VietnamSchool of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, VietnamInstitute of Informatics, Slovak Academy of Sciences, Bratislava, SlovakiaThe works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence. The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.https://ieeexplore.ieee.org/document/9072130/Global optimizationhybridizationevolution computationgalactic swarm optimizationwhale optimization algorithm |
| spellingShingle | Binh Minh Nguyen Trung Tran Thieu Nguyen Giang Nguyen Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem IEEE Access Global optimization hybridization evolution computation galactic swarm optimization whale optimization algorithm |
| title | Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem |
| title_full | Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem |
| title_fullStr | Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem |
| title_full_unstemmed | Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem |
| title_short | Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem |
| title_sort | hybridization of galactic swarm and evolution whale optimization for global search problem |
| topic | Global optimization hybridization evolution computation galactic swarm optimization whale optimization algorithm |
| url | https://ieeexplore.ieee.org/document/9072130/ |
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