A comprehensive review of krill herd algorithm: variants, hybrids and applications

Gai Ge Wang, Amirhossein Gandomi, Amir H. Alavi, Dunwei Gong

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

Original languageEnglish (US)
Pages (from-to)119-148
Number of pages30
JournalArtificial Intelligence Review
Volume51
Issue number1
DOIs
StatePublished - Jan 31 2019

Fingerprint

engineering
food
Benchmark
Food
Herding

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Language and Linguistics
  • Linguistics and Language

Cite this

Wang, Gai Ge ; Gandomi, Amirhossein ; Alavi, Amir H. ; Gong, Dunwei. / A comprehensive review of krill herd algorithm : variants, hybrids and applications. In: Artificial Intelligence Review. 2019 ; Vol. 51, No. 1. pp. 119-148.
@article{6658f235b00e43648d6f01e41da620b4,
title = "A comprehensive review of krill herd algorithm: variants, hybrids and applications",
abstract = "Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.",
author = "Wang, {Gai Ge} and Amirhossein Gandomi and Alavi, {Amir H.} and Dunwei Gong",
year = "2019",
month = "1",
day = "31",
doi = "https://doi.org/10.1007/s10462-017-9559-1",
language = "English (US)",
volume = "51",
pages = "119--148",
journal = "Artificial Intelligence Review",
issn = "0269-2821",
publisher = "Springer Netherlands",
number = "1",

}

A comprehensive review of krill herd algorithm : variants, hybrids and applications. / Wang, Gai Ge; Gandomi, Amirhossein; Alavi, Amir H.; Gong, Dunwei.

In: Artificial Intelligence Review, Vol. 51, No. 1, 31.01.2019, p. 119-148.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A comprehensive review of krill herd algorithm

T2 - variants, hybrids and applications

AU - Wang, Gai Ge

AU - Gandomi, Amirhossein

AU - Alavi, Amir H.

AU - Gong, Dunwei

PY - 2019/1/31

Y1 - 2019/1/31

N2 - Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

AB - Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

UR - http://www.scopus.com/inward/record.url?scp=85020750011&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020750011&partnerID=8YFLogxK

U2 - https://doi.org/10.1007/s10462-017-9559-1

DO - https://doi.org/10.1007/s10462-017-9559-1

M3 - Article

VL - 51

SP - 119

EP - 148

JO - Artificial Intelligence Review

JF - Artificial Intelligence Review

SN - 0269-2821

IS - 1

ER -