Multi-objective archived-based whale optimization algorithm

Nima Khodadadi; Seyedeh Zahra Mirjalili; Seyed Mohammad Mirjalili; Mohammad H. Nadim-Shahraki; Seyedali Mirjalili

doi:10.1016/B978-0-32-395365-8.00019-1

Multi-objective archived-based whale optimization algorithm

Nima Khodadadi, Seyedeh Zahra Mirjalili, Seyed Mohammad Mirjalili, Mohammad H. Nadim-Shahraki, Seyedali Mirjalili

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

2 Citations (Scopus)

Abstract

In this chapter, the Multi-Objective Archived-based Whale Optimization Algorithm (MAWOA) is a multi-objective version of the proposed WOA. It mimics the social behavior of humpback whales, and the algorithm is based on the bubble-net hunting strategy. The WOA algorithm incorporates three mechanisms, namely archive, grid, and leader selection, to facilitate multi-objective optimization. As a result of this research, the MAWOA has been developed to address multi-objective optimization issues that arise in various engineering problems. Eight engineering multi-objective optimization design problems are used to evaluate MAWOA. The algorithm's effectiveness is measured concerning multiple criteria, including coverage, generational distance, spacing, and others. The optimization results show that the MAWOA algorithm can compete favorably with the most advanced meta-heuristic algorithms.

Original language	English
Title of host publication	Handbook of Whale Optimization Algorithm
Subtitle of host publication	Variants, Hybrids, Improvements, and Applications
Publisher	Elsevier
Pages	169-177
Number of pages	9
ISBN (Electronic)	9780323953658
ISBN (Print)	9780323953641
DOIs	https://doi.org/10.1016/B978-0-32-395365-8.00019-1
Publication status	Published - 1 Jan 2023

Keywords

Multi-objective archived-based whale optimization algorithm
Real-world engineering problems
Whale optimization algorithm

Access to Document

10.1016/B978-0-32-395365-8.00019-1

Cite this

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title = "Multi-objective archived-based whale optimization algorithm",

abstract = "In this chapter, the Multi-Objective Archived-based Whale Optimization Algorithm (MAWOA) is a multi-objective version of the proposed WOA. It mimics the social behavior of humpback whales, and the algorithm is based on the bubble-net hunting strategy. The WOA algorithm incorporates three mechanisms, namely archive, grid, and leader selection, to facilitate multi-objective optimization. As a result of this research, the MAWOA has been developed to address multi-objective optimization issues that arise in various engineering problems. Eight engineering multi-objective optimization design problems are used to evaluate MAWOA. The algorithm's effectiveness is measured concerning multiple criteria, including coverage, generational distance, spacing, and others. The optimization results show that the MAWOA algorithm can compete favorably with the most advanced meta-heuristic algorithms.",

keywords = "Multi-objective archived-based whale optimization algorithm, Real-world engineering problems, Whale optimization algorithm",

author = "Nima Khodadadi and Mirjalili, {Seyedeh Zahra} and Mirjalili, {Seyed Mohammad} and Nadim-Shahraki, {Mohammad H.} and Seyedali Mirjalili",

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AU - Khodadadi, Nima

AU - Mirjalili, Seyedeh Zahra

AU - Mirjalili, Seyed Mohammad

AU - Nadim-Shahraki, Mohammad H.

AU - Mirjalili, Seyedali

PY - 2023/1/1

Y1 - 2023/1/1

N2 - In this chapter, the Multi-Objective Archived-based Whale Optimization Algorithm (MAWOA) is a multi-objective version of the proposed WOA. It mimics the social behavior of humpback whales, and the algorithm is based on the bubble-net hunting strategy. The WOA algorithm incorporates three mechanisms, namely archive, grid, and leader selection, to facilitate multi-objective optimization. As a result of this research, the MAWOA has been developed to address multi-objective optimization issues that arise in various engineering problems. Eight engineering multi-objective optimization design problems are used to evaluate MAWOA. The algorithm's effectiveness is measured concerning multiple criteria, including coverage, generational distance, spacing, and others. The optimization results show that the MAWOA algorithm can compete favorably with the most advanced meta-heuristic algorithms.

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Multi-objective archived-based whale optimization algorithm

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Keywords

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