Dynamic Adaptive Harmony Search Algorithm for Optimization

Abstract:

A new meta-heuristic optimization algorithm–harmony search is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to select suitable parameter values. In this paper, we proposed a novel algorithm to dynamically adapt the harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) and distance bandwidth (BW). The experimental results revealed the superiority of the proposed method to the original HS, improved harmony search (IHS) and global-best harmony search (GHS).

• Periodical: Key Engineering Materials (Volumes 474-476)
• Pages: 1666-1671
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.474-476.1666
• Online since: April 2011
• Authors: Yi Wen Wang, Min Yao
• Keywords: Evolutionary Algorithm (EA), Global Optimization, Harmony Search, Meta-Heuristic

References

[1] L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence Through Simulated Evolution, John Wiley, Chichester, UK, (1966).

[2] K. De Jong, Analysis of the behavior of a class of genetic adaptive systems, Ph.D. Thesis, University of Michigan, Ann Arbor, MI, (1975).

[3] J.R. Koza, Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems, Rep. No. STAN-CS-90-1314, Stanford University, CA, (1990).

[4] J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, (1975).

[5] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Boston, MA, (1989).

[6] F. Glover, Heuristic for integer programming using surrogate constraints, Decision Sci. 8 (1) (1977) 156–166.

DOI: 10.1111/j.1540-5915.1977.tb01074.x

[7] S. Kirkpatrick, C. Gelatt, M. Vecchi, Optimization by simulated annealing, Science 220 (4598) (1983) 671–680.

DOI: 10.1126/science.220.4598.671

[8] Z.W. Geem, J. -H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation 76 (2) (2001) 60–68.

DOI: 10.1177/003754970107600201

[9] K.S. Lee, Z.W. Geem, A new meta-heuristic algorithm for continues engineering optimization: harmony search theory and practice, Comput. Meth. Appl. Mech. Eng. 194 (2004) 3902–3933.

DOI: 10.1016/j.cma.2004.09.007

[10] Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188, 1567–1579.

DOI: 10.1016/j.amc.2006.11.033

[11] Omran, M. G., & Mahdavi, M. (2008). Global-best harmony search. Applied Mathematics and Computation, 198(2), 643–656.

DOI: 10.1016/j.amc.2007.09.004

[12] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.P. Chen, A. Auger, and S. Tiwari. Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Technicalreport, Nanyang Technological University, Singapore, May (2005).