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Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
book

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
October 2017
Intermediate to advanced content levelIntermediate to advanced
304 pages
8h 3m
English
Wiley
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

10 Harmony Search

Summary

This chapter describes harmony search (HS), which is a meta‐heuristic algorithm for discrete optimization. A brief literature review of the HS is presented, followed by a description of its algorithmic fundamentals. A pseudocode of the HS closes this chapter.

10.1 Introduction

Geem et al. (2001) developed harmony search (HS) inspired by the harmony found in many musical compositions. The HS has been applied to various benchmarking and real‐world optimization problems. Kim et al. (2001) implemented the HS for estimation of the nonlinear Muskingum model for flood routing. Geem et al. (2002) applied the HS to find optimal design of water distribution networks. Lee and Geem (2004) implemented the HS for structural optimization. Geem et al. (2009) reviewed the applications of the HS algorithm in the areas of water resources and environmental system optimization including design of water distribution networks, scheduling of multi‐location dams, parameter calibration of environmental models, and determination of ecological reserve location. Karahan et al. (2013) proposed a hybrid HS algorithm for the parameter estimation of the nonlinear Muskingum model. Ambia et al. (2015) applied the HS to optimally design the proportional–integral (PI) controllers of a grid‐side voltage converter with two additional loops for smooth transition of islanding and resynchronization operations in a distributed generation (DG) system.

10.2 Inspiration of the Harmony ...

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Publisher Resources

ISBN: 9781119386995Purchase book