Harmony Search as a Metaheuristic Algorithm

TL;DR

This paper reviews the Harmony Search algorithm, analyzing its core mechanics, characteristics, and similarities with other metaheuristics, and discusses potential improvements and future research directions.

Contribution

It provides a comprehensive analysis of Harmony Search as a metaheuristic, comparing it with other algorithms and suggesting avenues for development.

Findings

Harmony Search effectively balances exploration and exploitation.

HS shares similarities with particle swarm optimization.

Potential for developing improved variants of HS.

Abstract

This first chapter intends to review and analyze the powerful new Harmony Search (HS) algorithm in the context of metaheuristic algorithms. I will first outline the fundamental steps of Harmony Search, and how it works. I then try to identify the characteristics of metaheuristics and analyze why HS is a good meta-heuristic algorithm. I then review briefly other popular metaheuristics such as par-ticle swarm optimization so as to find their similarities and differences from HS. Finally, I will discuss the ways to improve and develop new variants of HS, and make suggestions for further research including open questions.