Harmony search algorithm for energy system applications: an updated review and analysis

Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Somayeh Asadi, Jin Hong Kim, Zong Woo Geem

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Recent advancements in energy systems have led to a series of new challenges in the decision-making process. Harmony search (HS) algorithm, which is a music-inspired optimisation technique, has been applied to some of these decision-making processes to obtain optimal set points within these energy systems. HS is based on the music improvisation process where musicians try to find better harmonies. Some of the advantages of HS method are that it is relatively simple to implement and require less algorithmic parameters. This paper aims to provide a comprehensive review on the applications of HS method to energy systems, that concentrate on two main objectives. First, the improved versions of HS introduced in recent studies will be reported. Second, contributed researches in energy systems by using HS will be analysed.

Original languageEnglish (US)
Pages (from-to)723-749
Number of pages27
JournalJournal of Experimental and Theoretical Artificial Intelligence
Volume31
Issue number5
DOIs
StatePublished - Sep 3 2019

Fingerprint

Harmony Search
Search Algorithm
Decision making
Energy
Music
Search Methods
Decision Making
Point Sets
Optimization Techniques
Review
Series

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence

Cite this

Nazari-Heris, Morteza ; Mohammadi-Ivatloo, Behnam ; Asadi, Somayeh ; Kim, Jin Hong ; Geem, Zong Woo. / Harmony search algorithm for energy system applications : an updated review and analysis. In: Journal of Experimental and Theoretical Artificial Intelligence. 2019 ; Vol. 31, No. 5. pp. 723-749.
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Harmony search algorithm for energy system applications : an updated review and analysis. / Nazari-Heris, Morteza; Mohammadi-Ivatloo, Behnam; Asadi, Somayeh; Kim, Jin Hong; Geem, Zong Woo.

In: Journal of Experimental and Theoretical Artificial Intelligence, Vol. 31, No. 5, 03.09.2019, p. 723-749.

Research output: Contribution to journalArticle

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