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

Centre for Artificial Intelligence Research and Optimisation (AIRO)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

15 Citations (Scopus)

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
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	407-419
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-031-09835-2_22
Publication status	Published - 2023

Publication series

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Keywords

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

Access to Document

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

Cite this

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title = "Multi-Objective Artificial Hummingbird Algorithm",

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.",

keywords = "Artificial hummingbird algorithm, Multi-objective artificial hummingbird algorithm, Real-world engineering",

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

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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

language = "English",

series = "Studies in Computational Intelligence",

publisher = "Springer Science and Business Media Deutschland GmbH",

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T1 - Multi-Objective Artificial Hummingbird Algorithm

AU - Khodadadi, Nima

AU - Mirjalili, Seyed Mohammad

AU - Zhao, Weiguo

AU - Zhang, Zhenxing

AU - Wang, Liying

AU - Mirjalili, Seyedali

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Artificial hummingbird algorithm

KW - Multi-objective artificial hummingbird algorithm

KW - Real-world engineering

UR - https://www.scopus.com/pages/publications/85139430921

U2 - 10.1007/978-3-031-09835-2_22

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

M3 - Chapter

AN - SCOPUS:85139430921

T3 - Studies in Computational Intelligence

SP - 407

EP - 419

BT - Studies in Computational Intelligence

PB - Springer Science and Business Media Deutschland GmbH

ER -

Multi-Objective Artificial Hummingbird Algorithm

Abstract

Publication series

UN SDGs

Keywords

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