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A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology

Copyright © 2012. 12 pages.
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DOI: 10.4018/978-1-4666-0270-0.ch005
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MLA

Weyland, Dennis. "A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology." Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends. IGI Global, 2012. 72-83. Web. 20 Apr. 2014. doi:10.4018/978-1-4666-0270-0.ch005

APA

Weyland, D. (2012). A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology. In P. Yin (Ed.), Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends (pp. 72-83). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-0270-0.ch005

Chicago

Weyland, Dennis. "A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology." In Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends, ed. Peng-Yeng Yin, 72-83 (2012), accessed April 20, 2014. doi:10.4018/978-1-4666-0270-0.ch005

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Abstract

In recent years a lot of novel (mostly naturally inspired) search heuristics have been proposed. Among those approaches is Harmony Search. After its introduction in 2000, positive results and improvements over existing approaches have been reported. In this paper, the authors give a review of the developments of Harmony Search during the past decade and perform a rigorous analysis of this approach. This paper compares Harmony Search to the well-known search heuristic called Evolution Strategies. Harmony Search is a special case of Evolution Strategies in which the authors give compelling evidence for the thesis that research in Harmony is fundamentally misguided. The overarching question is how such a method could be inaccurately portrayed as a significant innovation without confronting a respectable challenge of its content or credentials. The authors examine possible answers to this question, and implications for evaluating other procedures by disclosing the way in which limitations of the method have been systematically overlooked.
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Harmony Search (HS) is a search heuristic based on the improvisation process of jazz musicians (Geem et al., 2001). In jazz music the different musicians try to adjust their pitches, such that the overall harmonies are optimized due to aesthetic objectives. Starting with some harmonies, they attempt to achieve better harmonies by improvisation. This analogy can be used to derive search heuristics, which can be used to optimize a given objective function instead of harmonies. Here the musicians are identified with the decision variables and the harmonies correspond to solutions. Like jazz musicians create new harmonies by improvisation, the HS algorithm creates iteratively new solutions based on past solutions and on random modifications. While this framework leaves a lot of space for interpretation, the basic HS algorithm is always described in the literature in the following way.

The HS algorithm initializes the Harmony Memory (HM) with randomly generated solutions. The number of solutions stored in the HM is defined by the Harmony Memory Size (HMS). Then iteratively a new solution is created as follows. Each decision variable is generated either on memory consideration and a possible additional modification, or on random selection. The parameters that are used in the generation process of a new solution are called Harmony Memory Considering Rate (HMCR) and Pitch Adjusting Rate (PAR). Each decision variable is set to the value of the corresponding variable of one of the solutions in the HM with a probability of HMCR, and an additional modification of this value is performed with a probability of PAR. Otherwise (with a probability of 1-HMCR), the decision variable is set to a random value. After a new solution has been created, it is evaluated and compared to the worst solution in the HM. If its objective value is better than that of the worst solution, it replaces the worst solution in the HM. This process is repeated, until a termination criterion is fulfilled. More detailed descriptions of this algorithm can be found in Geem et al. (2005c), Mahdavi et al. (2007), and Geem (2005a). Algorithm 1 gives an overview about the HS algorithm using pseudo code.

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Complete Chapter List

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Table of Contents
Preface
Peng-Yeng Yin
Chapter 1
Fred Glover, Saïd Hanafi
Recent adaptive memory and evolutionary metaheuristics for mixed integer programming have included proposals for introducing inequalities and target... Sample PDF
Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization Part I: Exploiting Proximity
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Chapter 2
Fred Glover, Saïd Hanafi
Recent metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide this search.... Sample PDF
Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization – Part II: Exploiting Reaction and Resistance
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Chapter 3
Ender Özcan, Mustafa Misir, Gabriela Ochoa, Edmund K. Burke
Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search... Sample PDF
A Reinforcement Learning: Great-Deluge Hyper-Heuristic for Examination Timetabling
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Chapter 4
Ahmed Mellouli, Faouzi Masmoudi, Imed Kacem, Mohamed Haddar
In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this... Sample PDF
A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem
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Chapter 5
Dennis Weyland
In recent years a lot of novel (mostly naturally inspired) search heuristics have been proposed. Among those approaches is Harmony Search. After its... Sample PDF
A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a “Novel” Methodology
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Chapter 6
Zong Woo Geem
Recently a paper was published which claims “harmony search is equivalent to evolution strategies and because the latter is not popular currently... Sample PDF
Research Commentary Survival of the Fittest Algorithm or the Novelest Algorithm?: The Existence Reason of the Harmony Search Algorithm
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Chapter 7
Masoud Yaghini, Mohammad Rahim Akhavan Kazemzadeh
Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order... Sample PDF
DIMMA: A Design and Implementation Methodology for Metaheuristic Algorithms - A Perspective from Software Development
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Chapter 8
S. Nguyen, V. Kachitvichyanukul
Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason... Sample PDF
Movement Strategies for Multi-Objective Particle Swarm Optimization
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Chapter 9
Julien Lepagnot, Amir Nakib, Hamouche Oulhadj, Patrick Siarry
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A New Multiagent Algorithm for Dynamic Continuous Optimization
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Chapter 10
M. Fakhfakh, S. Masmoudi, Y. Cooren, M. Loulou, P. Siarry
This paper presents the optimal design of a switched current sigma delta modulator. The Multi-objective Particle Swarm Optimization technique is... Sample PDF
Improving Switched Current Sigma Delta Modulators’ Performances via the Particle Swarm Optimization Technique
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Chapter 11
Giglia Gómez-Villouta, Jean-Philippe Hamiez, Jin-Kao Hao
This paper discusses a particular “packing” problem, namely the two dimensional strip packing problem, where a finite set of objects have to be... Sample PDF
A Reinforced Tabu Search Approach for 2D Strip Packing
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Chapter 12
Jean-Philippe Hamiez, Jin-Kao Hao, Fred W. Glover
The authors present an experimental investigation of tabu search (TS) to solve the 3-coloring problem (3-COL). Computational results reveal that a... Sample PDF
A Study of Tabu Search for Coloring Random 3-Colorable Graphs Around the Phase Transition
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Chapter 13
Houra Mahmoudzadeh, Kourosh Eshghi
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A Metaheuristic Approach to the Graceful Labeling Problem
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Chapter 14
Joaquín Derrac, Salvador García, Francisco Herrera
The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining... Sample PDF
A Survey on Evolutionary Instance Selection and Generation
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Chapter 15
Yingxu Wang
In studies of genetic algorithms, evolutionary computing, and ant colony mechanisms, it is recognized that the higher-order forms of collective... Sample PDF
A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing
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Chapter 16
Bahram Alidaee, Gary Kochenberger, Haibo Wang
Modern metaheuristic methodologies rely on well defined neighborhood structures and efficient means for evaluating potential moves within these... Sample PDF
Theorems Supporting r-flip Search for Pseudo-Boolean Optimization
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Chapter 17
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Stochastic Learning for SAT- Encoded Graph Coloring Problems
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Chapter 18
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Chapter 19
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Chapter 20
Robert Wille, Rolf Drechsler
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BDD-Based Synthesis of Reversible Logic
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