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Harmony Search as a Metaheuristic Algorithm
Xin-She Yang
Department of Engineering, University of Cambridge, Trumpington Street,
Cambridge CB2 1PZ, UK
xy227@cam.ac.uk
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
 
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