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 proceedingChapterpeer-review

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 languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Science and Business Media Deutschland GmbH
Pages407-419
Number of pages13
DOIs
Publication statusPublished - 2023

Publication series

NameStudies in Computational Intelligence
Volume1054
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Keywords

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

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