Harmony Search as a Metaheuristic Algorithm

This first chapter intends to review and analyze the powerful new Harmony Search (HS) algorithm in the context of metaheuristic algorithms. We will first outline the fundamental steps of HS, and show how it works. We then try to identify the characteristics of metaheuristics and analyze why HS is a good metaheuristic algorithm. We then review briefly other popular metaheuristics such as particle swarm optimization so as to find their similarities and differences with HS. Finally, we will discuss the ways to improve and develop new variants of HS, and make suggestions for further research including open questions.

[1]  Zong Woo Geem,et al.  Harmony Search Algorithm for Solving Sudoku , 2007, KES.

[2]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

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

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

[5]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

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

[7]  Zong Woo Geem,et al.  Optimal Scheduling of Multiple Dam System Using Harmony Search Algorithm , 2007, IWANN.

[8]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[9]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

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

[12]  Kenneth de Jong,et al.  Evolutionary computation: a unified approach , 2007, GECCO.

[13]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[14]  A. Ostfeld,et al.  An adaptive heuristic cross-entropy algorithm for optimal design of water distribution systems , 2007 .

[15]  Z. Geem.  Optimal Design of Water Distribution Networks Using Harmony Search , 2009 .

[16]  Xin-She Yang,et al.  Biology-Derived Algorithms in Engineering Optimization , 2010, Handbook of Bioinspired Algorithms and Applications.

[17]  Mahamed G. H. Omran,et al.  Global-best harmony search , 2008, Appl. Math. Comput..

[18]  James C. Spall,et al.  Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control (Spall, J.C. , 2007 .

[19]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[20]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[21]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[22]  Max E. Valentinuzzi.  Handbook of bioinspired algorithms and applications , 2006, BioMedical Engineering OnLine.

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  James C. Spall,et al.  Introduction to stochastic search and optimization - estimation, simulation, and control , 2003, Wiley-Interscience series in discrete mathematics and optimization.

[25]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .