Modified Harmony Search Methods for Uni-Modal and Multi-Modal Optimization

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. In this paper, we propose two modified HS methods to deal with the uni-modal and multi-modal optimization problems. The first modified HS method is based on the fusion of the HS and differential evolution (DE) technique, namely, HS-DE. The DE is employed here to optimize the members of the HS memory. The second modified HS method utilizes a novel HS memory management approach, and it targets at handling the multi-modal problems. Several nonlinear functions are used to demonstrate and verify the effectiveness of our two new HS methods.

[1]  Xiaolei Wang,et al.  A novel particle swarm-based method for nonlinear function optimization , 2008 .

[2]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[3]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[4]  Zong Woo Geem,et al.  Harmony Search Optimization: Application to Pipe Network Design , 2002 .

[5]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[6]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[7]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[8]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[9]  Hisao Ishibuchi,et al.  Hybrid Evolutionary Algorithms , 2007 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Jian-Wei Ma,et al.  An improved artificial fish-swarm algorithm and its application in feed-forward neural networks , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[14]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer and its adaptive variant , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[16]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[17]  Xiao Zhi Gao,et al.  A Hybrid Optimization Algorithm Based on Ant Colony and Immune Principles , 2007, Int. J. Comput. Sci. Appl..