A Modified Harmony Search Threshold Accepting Hybrid Optimization Algorithm
- 722 Downloads
Hybrid metaheuristics are the recent trend that caught the attention of several researchers which are more efficient than the metaheuristics in finding the global optimal solution in terms of speed and accuracy. This paper presents a novel optimization metaheuristic by hybridizing Modified Harmony Search (MHS) and Threshold Accepting (TA) algorithm. This methodology has the advantage that one metaheuristic is used to explore the entire search space to find the area near optima and then other metaheuristic is used to exploit the near optimal area to find the global optimal solution. In this approach Modified Harmony Search was employed to explore the search space whereas Threshold Accepting algorithm was used to exploit the search space to find the global optimum solution. Effectiveness of the proposed hybrid is tested on 22 benchmark problems. It is compared with the recently proposed MHS+MGDA hybrid. The results obtained demonstrate that the proposed methodology outperforms the MHS and MHS+MGDA in terms of accuracy and functional evaluations and can be an expeditious alternative to MHS and MHS+MGDA.
KeywordsHarmony Search Threshold Accepting Hybrid Metaheuristic Unconstrained Optimization Metaheuristic
Unable to display preview. Download preview PDF.
- 1.Choudhuri, R., Ravi, V., Mahesh Kumar, Y.: A Hybrid Harmony Search and Modified Great Deluge Algorithm for Unconstrained Optimization. Int. Jo. of Comp. Intelligence Research 6(4), 755–761 (2010)Google Scholar
- 13.Li, H., Li, L.: A novel hybrid particle swarm optimization algorithm combined with harmony search for higher dimensional optimization problems. In: Int. Conference on Intelligent Pervasive Computing, Jeju Island, Korea (2007)Google Scholar
- 15.Gao, X.Z., Wang, X., Ovaska, J.: Uni-Modal and Multi Modal optimization using modified harmony search methods. IJICIC 5(10(A)), 2985–2996 (2009)Google Scholar
- 22.Dixon, L., Szego, G.: Towards Global Optimization 2. North Holland, New York (1978)Google Scholar
- 27.Muhlenbein, H., Schomisch, S., Born, J.: The parallel genetic algorithm as function optimizer. In: Belew, R., Booker, L. (eds.) Proceedings of the Fourth Int. Conference on Genetic Algorithms, pp. 271–278. Morgan Kaufmann (1991)Google Scholar
- 28.Sphere problem; global and local optima, http://www.optima.amp.i.kyoto.ac.jp/member/student/hedar/Hedar_files/TestGO_files/Page113.html (cited on November 20, 2010)
- 29.Zakharov Problem Global and local optima, www.optima.amp.i.kyotoc.jp/member/student/hedar/Hedar_files/TestGO_files/Page3088.htm (cited on November 20, 2010)