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Harmony Search/Particle Swarm Optimization Hybrid Optimization Algorithm and Its Application in Function Optimization
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Harmony Search/Particle Swarm Optimization 하이브리드 최적화 알고리즘 및 함수 최적화

논문 기본 정보

Type
Academic journal
Author
Tae-Bong Lee (가천대학교)
Journal
Korean Institute of Intelligent Systems Journal of Korean Institute of Intelligent Systems Vol.34 No.6 KCI Accredited Journals
Published
2024.12
Pages
556 - 563 (8page)
DOI
10.5391/JKIIS.2024.34.6.556

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Harmony Search/Particle Swarm Optimization Hybrid Optimization Algorithm and Its Application in Function Optimization
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Abstract· Keywords

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The performance of a meta-heuristic algorithm is dependent on a balance between exploration of the entire variable range and exploitation of existing preliminary solutions. However, the inefficiency of pitch adjustment, which is a key function in finding the solution, can be considered a disadvantage. On the other hand, PSO, which uses the experience of the population to find a solution, is known to have excellent convergence. However, it has the disadvantage that the search is only done with combinations of the population, so there is a high possibility of convergence to a local solution. HS/PSO aims to combine the exploration excellence of HS with the good convergence of PSO. To this end, HS/PSO defines a new harmony composition that follows the HS method, but instead of inefficient pitch adjustment, a guide vector is defined. The guide vector is a vector created by using the current highest and lowest positions in HM as experience with reference to the PSO method. This guide vector was applied to the HM method harmony to replace the HS pitch adjustment. The results of this study were compared and verified through optimization examples for various functions, and its superiority was confirmed.
#탐색성 #탐지성 #meta-heuristic #exploration #exploitation #HS #PSO #HS/PSO

Contents

요약
Abstract
1. 서론
2. HS 알고리즘
3. PSO
4. hybrid HS/PSO
5. 수치 예와 성능 평가
6. 결론
Reference

References (17)

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X. S. Yang, Nature-inspired metaheuristic algorithms, Luniver Press, UK. 2008. google schola Z.W. Geem, J.H. Kim and G.V. Loganathan, “A new heuristic optimization algorithm: Harmony search,” Simulation, 76, pp. 60–68., 2001. google schola M. Mahdavi, M. Fesanghary and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Appl. Math. Comput. vol. 188, pp. 1567–1579, 2007. google schola M. Damangir and M. Mahdavi, “Global-best harmony search,” Appl. Math. Comput. vol. 198, pp. 643–656, 2008. google schola Feng Qin, Mohd Zain Azlan, and Kai-Qing Zhou, “Harmony search algorithm and related variants: A systematic review,” Swarm and Evolutionary Computation, vol. 74, art. 101126, doi:10.1016/j.swevo, 2022. google schola

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