Browse > Article
http://dx.doi.org/10.12673/jant.2021.25.5.403

A Study on the Performance Improvement of Harmony Search Optimization Algorithm  

Lee, Tae-Bong (Department of Electronic Engineering, Gachon University)
Abstract
Harmony Search(HS) algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and has been successfully applied to solve different optimization problems. In order to further improve the performance of HS, this paper proposes a new method which is called Fast Harmony Search(FSH) algorithm. For the purpose, this paper suggest a method to unify two independent improvisation processes by newly defining the boundary value of a object variable using HM. As the result, the process time of the algorithm is shorten and explicit decision of bandwidth is no more needed. Furthermore, exploitative power of random selection is improved. The numerical results reveal that the proposed algorithm can find better solutions and is faster when compared to the conventional HS.
Keywords
FHS; HS; Meta-heuristic; Optimization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing. New York: Springer-Verlag, 2003.
2 GOLDBERG, David E.; HOLLAND, John H. Genetic algorithms and machine learning, Machine learning, 1988, 3.2: pp.95-99.   DOI
3 MAHDAVI, Mehrdad; FESANGHARY, Mohammad; DAMANGIR, E. An improved harmony search algorithm for solving optimization problems, Applied mathematics and computation, Vol.188, No.2: pp.1567-1579, 2007.   DOI
4 T. B. Lee, "Analytic Approach to Determin Algorithm Parameters of HS Optimization," Trans. of KIEE vol. 68, no. 4, pp. 200-206, 2019.   DOI
5 M. Dorigo and G. Di Caro, "Ant colony optimization: a new meta-heuristic," Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999, pp. 1470-1477, Vol. 2.
6 WANG, Chia-Ming; HUANG, Yin-Fu. Self-adaptive harmony search algorithm for optimization, Expert Systems with Applications, Vol.37, No.4, pp.2826-2837, 2010.   DOI
7 T. Back, D. Fogel, and Z. Michalewicz, Handbook of Evolutionary Computation. London, U.K.: xford Univ. Press, 1997.
8 A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence. Hoboken, NJ: Wiley, 2006.
9 J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence. San Francisco, CA: Morgan Kaufmann, 2001.
10 EBERHART, Russell; KENNEDY, James. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. 1995. pp.1942-1948.
11 Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: Harmony search," J. Simul., vol. 76, no. 2, pp. 60-68, Feb. 2001.   DOI
12 A. A. Al-Omoush, A. A. Alsewari, H. S. Alamri and K. Z. Zamli, "Comprehensive Review of the Development of the Harmony Search Algorithm and its Applications," in IEEE Access, vol. 7, pp. 14233-14245, 2019,   DOI
13 OMRAN, Mahamed GH; MAHDAVI, Mehrdad. Global-best harmony search, Applied mathematics and computation, Vol.198, No.2: pp.643-656, 2008,   DOI
14 Slowik, A., Kwasnicka, H. Evolutionary algorithms and their applications to engineering problems. Neural Comput. & Applic. 32, 12363-12379, 2020.   DOI