A Self-Study Harmony Search Algorithm for Optimization Problems

Abstract:

A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS)

• Periodical: Advanced Materials Research (Volumes 1006-1007)
• Pages: 1017-1020
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1006-1007.1017
• Online since: August 2014
• Authors: Ping Zhang*, Mei Ling Li, Qian Han, Guo Jun Li
• Keywords: Harmony Search Algorithm, Self-Study Harmony Search, Self-Study Strategy

References

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

[2] Fred Glover, Harvey J. Greenberg. New approaches for heuristic search: A bilateral linkage with artificial intelligence. European Journal of Operational Research 1989, 39(2): 119-130.

DOI: 10.1016/0377-2217(89)90185-9

[3] S. Kirkpatrick, C. Gelatt, M. Vecchi. Optimization by simulated annealing. Science 220, 1983, pp : 671-680.

DOI: 10.1126/science.220.4598.671

[4] M. Dorigo, V. Maniezzo, A. Golomi, Ant system: optimization by a colony of cooperation agents, IEEE Transactions on SMC, 1996, 26(1): 29-41.

[5] J. Kennedy. R. C. Eberhart. Particle swarm optimization, in: Proceeding of IEEE International Conference on Neural Networks, 1995, p.1942-(1948).

[6] R. storn, Differential evolution design of an IIR-filter, in: IEEE International Conference on Evolutionary Computation, Nagoya, 1996, pp.268-273.

DOI: 10.1109/icec.1996.542373

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

DOI: 10.1177/003754970107600201

[8] Mahdavi M, Fesanghary M, Damangir E. An improved harmony search algorithm for solving optimization problems [J], Applied Mathematics and Computation, 2007, 188(2): 1567–1579.

DOI: 10.1016/j.amc.2006.11.033

[9] Chia-Ming Wang, Yin-Fu Huang. Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications, 2010, 37(4): 2826-2837.

DOI: 10.1016/j.eswa.2009.09.008

[10] Zong Woo Geem, Kwee-Bo Sim. Parameter-setting-free harmony search algorithm. Applied Mathematics and Computation, 2010, 217(8): 3881-3889.

DOI: 10.1016/j.amc.2010.09.049

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

DOI: 10.1016/j.amc.2007.09.004

[12] Pan Q K, Suganthan P N, Tasgetiren M F, Liang J J. A self-adaptive global best harmony search algorithm for continuous optimization problems [J], Applied Mathematics and Computation, 2010, 216(3): 830-848.

DOI: 10.1016/j.amc.2010.01.088

[13] Zou D X, Gao L Q, Wu J H, Li S. Novel global harmony search algorithm for unconstrained problems [J], Neurocomputing, 2010, 73(16-18): 3308-3318.

DOI: 10.1016/j.neucom.2010.07.010