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Harmony Search Algorithms for binary optimization problems

  • Miriam Padberg
Conference paper
Part of the Operations Research Proceedings book series (ORP)

Abstract

In many theoretical and empirical investigations heuristic methods are the subject of investigation for optimization problems in order to get good and valid solutions. In 2001 Harmony Search, a new music-inspired meta-heuristic, was introduced [1]. It has been applied to various types of optimization problems, for example structural design problems, showing significant improvements over other heuristics. Motivated by the question whether these heuristics represent a new class of meta-heuristics or whether they are only another representation of a well-known technique, a couple of investigations were made. In this paper we will show that for the class of binary optimization problems this new nature-inspired heuristic is ”equivalent” to an evolutionary algorithm.

Keywords

Evolutionary Algorithm Random Selection Solution Vector Pitch Adjustment Memory Consideration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Geem, Kim and Loganathan: A New Heuristic Optimization Algorithm: Harmony Search, Simulation 76 (2), pages 60–68 (2001)Google Scholar
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    Geem and Lee: A new meta-heuristic algorithm for continuous engineering optimization: harmony search in theory and practice, ComputerMethods in applied mechanics and engineering 195, pages 3902–3933 (2004)Google Scholar
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    Weyland: A Rigourous Analysis of the Harmony Search Algorithm: How the Research Community can beMisled by a ’Novel’Methodology,International Journal of AppliedMetaheuristic Computing (2010)Google Scholar
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    Spears: Evolutionary Algorithms, Natural Computating Series, Springer (2000)Google Scholar
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    Beyer and Schwefel: Evolution strategies - A comprehensive introduction, Natural Computing 1(1), pages 3–53 (2002)Google Scholar
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    Baeck and Schwefel: An overview of evolutionary algorithms for parameter optimization, Evolutionary computation 1(1), pages 1–23 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.TUDortmundGermany

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