Music-Inspired Harmony Search Algorithm Theory andApplications

210 Pages·2009·5.918 MB·English

by  ZongWoo Geem (ed.)

ZongWooGeem(Ed.)
Music-InspiredHarmonySearchAlgorithm
StudiesinComputationalIntelligence,Volume191
Editor-in-Chief
Dr.ZongWooGeem
Westat
1650ResearchBlvd.TA1105
Rockville,Maryland20850
USA
E-mail:[e-mail]
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ZongWoo Geem
(Ed.)
Music-Inspired Harmony Search
Algorithm
Theory and Applications
123
Dr.ZongWooGeem
Westat
1650ResearchBlvd.TA1105
Rockville,Maryland20850
USA
E-mail:[e-mail]
ISBN978-3-642-00184-0 e-ISBN978-3-642-00185-7
DOI10.1007/978-3-642-00185-7
StudiesinComputationalIntelligence ISSN1860949X
LibraryofCongressControlNumber:2008944108
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Preface
Calculus has been used in solving many scientific and engineering problems. For
optimization problems, however, the differential calculus technique sometimes has a
drawback when the objective function is step-wise, discontinuous, or multi-modal, or
when decision variables are discrete rather than continuous. Thus, researchers have
recently turned their interests into metaheuristic algorithms that have been inspired by
natural phenomena such as evolution, animal behavior, or metallic annealing.
This book especially focuses on a music-inspired metaheuristic algorithm, harmony
search. Interestingly, there exists an analogy between music and optimization: each
musical instrument corresponds to each decision variable; musical note corresponds to
variable value; and harmony corresponds to solution vector. Just like musicians in
Jazz improvisation play notes randomly or based on experiences in order to find fan-
tastic harmony, variables in the harmony search algorithm have random values or
previously-memorized good values in order to find optimal solution.
The recently-developed harmony search algorithm has been vigorously applied to
various optimization problems. Thus, the goal of this book is to show readers full
spectrum of the algorithm in theory and applications in the form of an edited volume
with the following subjects: justification as a metaheuristic algorithm by Yang; litera-
ture review by Ingram and Zhang; multi-modal approach by Gao, Wang & Ovaska;
computer science applications by Mahdavi; engineering applications by Fesanghary;
structural design by Saka; water and environmental applications by Geem, Tseng &
Williams; groundwater modeling by Ayvaz; geotechnical analysis by Cheng; energy
demand forecasting by Ceylan; sound classification in hearing aids by Alexandre,
Cuadra & Gil-Pita; and therapeutic medical physics by Panchal.
As an editor of this book, I’d like to express my deepest thanks to reviewers and
proofreaders including Mike Dreis, John Galuardi, Sanghun Kim, Una-May O’Reilly,
Byungkyu Park, Ronald Wiles, and Ali Rıza Yıldız, as well as the above-mentioned
chapter authors. Furthermore, as a first inventor of the harmony search algorithm, I espe-
cially thank Joel Donahue, Chung-Li Tseng, Joong Hoon Kim, and the late G. V. Loga-
nathan (victim of Virginia Tech shooting) for their ideas and support. Finally, I’d like to
share the joy of the publication with my family who are unceasing motivators in life.

Zong Woo Geem
Editor
Synopsis
Recently music-inspired harmony search algorithm has been proposed and vigorously
applied to various scientific and engineering applications such as music composition,
Sudoku puzzle solving, tour planning, web page clustering, structural design, water
network design, vehicle routing, dam scheduling, ground water modeling, soil stability
analysis, ecological conservation, energy system dispatch, heat exchanger design,
transportation energy modeling, pumping operation, model parameter calibration,
satellite heat pipe design, medical physics, etc.
However, these applications of the harmony search algorithm are dispersed in vari-
ous journals, proceedings, degree theses, technical reports, books, and magazines,
which makes readers difficult to draw a big picture of the algorithm. Thus, this book is
designed to putting together all the latest developments and cutting-edge studies of
theoretical backgrounds and practical applications of the harmony search algorithm
for the first time, in order for readers to efficiently understand a full spectrum of the
algorithm and to foster new breakthroughs in their fields using the algorithm.
Contents
Harmony Search as a Metaheuristic Algorithm
Xin-She Yang .................................................... 1
Overview of Applications and Developments in the Harmony
Search Algorithm
Gordon Ingram, Tonghua Zhang .................................... 15
Harmony Search Methods for Multi-modal and Constrained
Optimization
X.Z. Gao, X. Wang, S.J. Ovaska ................................... 39
Solving NP-Complete Problems by Harmony Search
Mehrdad Mahdavi................................................. 53
Harmony Search Applications in Mechanical, Chemical and
Electrical Engineering
Mohammad Fesanghary............................................ 71
Optimum Design of Steel Skeleton Structures
Mehmet Polat Saka ............................................... 87
Harmony Search Algorithms for Water and Environmental
Systems
Zong Woo Geem, Chung-Li Tseng, Justin C. Williams................. 113
Identification of Groundwater Parameter Structure Using
Harmony Search Algorithm
M. Tamer Ayvaz.................................................. 129
Modified Harmony Methods for Slope Stability Problems
Yung-Ming Cheng................................................. 141
X Contents
Harmony Search Algorithm for Transport Energy Demand
Modeling
Halim Ceylan, Huseyin Ceylan...................................... 163
Sound Classification in Hearing Aids by the Harmony Search
Algorithm
Enrique Alexandre, Lucas Cuadra, Roberto Gil-Pita ................... 173
Harmony Search in Therapeutic Medical Physics
Aditya Panchal ................................................... 189
Author Index................................................... 205
Harmony Search as a Metaheuristic Algorithm
Xin-She Yang
Department of Engineering, University of Cambridge, Trumpington Street,
Cambridge CB2 1PZ, UK
[e-mail]
Abstract. 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.
Keywords: Harmony Search, Metaheuristic Algorithms, Diversification, Intensification,
Optimization.
1 Introduction
When listening to a beautiful piece of classical music, who has ever wondered if there
is any connection between playing music and finding an optimal solution to a tough
design problem such as the water network design or other problems in engineering?
Now for the first time ever, scientists have found such an interesting connection by
developing a new algorithm, called Harmony Search. HS was first developed by
Geem et al. in 2001 [1]. Though it is a relatively new metaheuristic algorithm, its ef-
fectiveness and advantages have been demonstrated in various applications. Since its
first appearance in 2001, it has been applied to many optimization problems including
function optimization, engineering optimization, design of water distribution net-
works, groundwater modeling, energy-saving dispatch, truss design, vehicle routing,
and others [2, 3]. The possibility of combining harmony search with other algorithms
such as Particle Swarm Optimization has also been investigated.
Harmony search is a music-based metaheuristic optimization algorithm. It was in-
spired by the observation that the aim of music is to search for a perfect state of har-
mony. The effort to find the harmony in music is analogous to find the optimality in
an optimization process. In other words, a jazz musician’s improvisation process can
be compared to the search process in optimization. On one hand, the perfectly pleas-
ing harmony is determined by the audio aesthetic standard. A musician always intends
to produce a piece of music with perfect harmony. On the other hand, an optimal solu-
tion to an optimization problem should be the best solution available to the problem
under the given objectives and limited by constraints. Both processes intend to
produce the best or optimum.
Such similarities between two processes can be used to develop a new algorithm
by learning from each other. Harmony Search is just such a successful example by
transforming the qualitative improvisation process into quantitative optimization
Z.W. Geem (Ed.): Music-Inspired Harmony Search Algorithm, SCI 191, pp. 1–14.
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