Multi-Objective Artificial Hummingbird Algorithm

Nima Khodadadi; Seyed Mohammad Mirjalili; Weiguo Zhao; Zhenxing Zhang; Liying Wang; Seyedali Mirjalili

doi:10.1007/978-3-031-09835-2_22

Multi-Objective Artificial Hummingbird Algorithm

Nima Khodadadi, Seyed Mohammad Mirjalili, Weiguo Zhao, Zhenxing Zhang, Liying Wang, Seyedali Mirjalili

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This chapter introduces Multi-Objective Artificial Hummingbird Algorithm (MOAHA), a multi-objective variation of the newly established Artificial Hummingbird Algorithm (AHA). The AHA algorithm simulates the specific flight skills and intelligent search strategies of hummingbirds in the wild. Three types of flight skills are used in food search strategies, including axial, oblique, and all-round flights. Multi-objective AHA is tested through 5 real-world engineering case studies. Various performance indicators, such as Spacing (S), Inverted Generational Distance (IGD), and Maximum Spread (MS), are used to compare the MOAHA to the MOPSO, MOWOA, and MOHHO. The suggested algorithm may produce quality Pareto fronts with appropriate precision, uniformity, and very competitive outcomes, according to the qualitative and quantitative.

Original language	English (US)
Title of host publication	Studies in Computational Intelligence
Publisher	Springer
Pages	407-419
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-031-09835-2_22
State	Published - 2023

Publication series

Name	Studies in Computational Intelligence
Volume	1054
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Keywords

Artificial hummingbird algorithm
Multi-objective artificial hummingbird algorithm
Real-world engineering

ASJC Scopus subject areas

Artificial Intelligence

Online availability

10.1007/978-3-031-09835-2_22

Library availability

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Cite this

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abstract = "This chapter introduces Multi-Objective Artificial Hummingbird Algorithm (MOAHA), a multi-objective variation of the newly established Artificial Hummingbird Algorithm (AHA). The AHA algorithm simulates the specific flight skills and intelligent search strategies of hummingbirds in the wild. Three types of flight skills are used in food search strategies, including axial, oblique, and all-round flights. Multi-objective AHA is tested through 5 real-world engineering case studies. Various performance indicators, such as Spacing (S), Inverted Generational Distance (IGD), and Maximum Spread (MS), are used to compare the MOAHA to the MOPSO, MOWOA, and MOHHO. The suggested algorithm may produce quality Pareto fronts with appropriate precision, uniformity, and very competitive outcomes, according to the qualitative and quantitative.",

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author = "Nima Khodadadi and Mirjalili, {Seyed Mohammad} and Weiguo Zhao and Zhenxing Zhang and Liying Wang and Seyedali Mirjalili",

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doi = "10.1007/978-3-031-09835-2_22",

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AU - Khodadadi, Nima

AU - Mirjalili, Seyed Mohammad

AU - Zhao, Weiguo

AU - Zhang, Zhenxing

AU - Wang, Liying

AU - Mirjalili, Seyedali

PY - 2023

Y1 - 2023

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Multi-Objective Artificial Hummingbird Algorithm

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