research-article

Bee-inspired metaheuristics for global optimization: a performance comparison

Authors:

Hugo A. LoáicigaAuthors Info & Claims

Artificial Intelligence Review, Volume 54, Issue 7

Pages 4967 - 4996

https://doi.org/10.1007/s10462-021-10015-1

Published: 01 October 2021 Publication History

Abstract

Metaheuristics are widely applied to solve optimization problems. Numerous metaheuristic algorithms inspired by natural processes have been introduced in the past years. Studying and comparing the convergence of metaheuristics is helpful in future algorithmic development and applications. This study focuses on bee-inspired metaheuristics and identifies seven basic or root algorithms applied to solve continuous optimization problems. They are the bee system, mating bee optimization (MBO), bee colony optimization, bee evolution for genetic algorithms (BEGA), bee algorithm, artificial bee colony (ABC), and bee swarm optimization. The algorithms’ performances are evaluated with several benchmark problems. This study’s results rank the cited algorithms according to their convergence efficiency. The strengths and shortcomings of each algorithm are discussed. The ABC, BEGA, and MBO are the most efficient algorithms. This study’s results show the convergence rate among different algorithms varies, and evaluates the causes of such variation.

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Cited By

Khalfi SCaraffini FIacca G(2023)Metaheuristics in the BalanceInternational Journal of Intelligent Systems10.1155/2023/57080852023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/5708085
Palominos POrtega CAlfaro MFuertes GVargas MCamargo MParada VGatica G(2022)Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson ProblemComplexity10.1155/2022/89030052022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8903005

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Bee-inspired metaheuristics for global optimization: a performance comparison

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 54, Issue 7

Oct 2021

795 pages

ISSN:0269-2821

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© The Author(s), under exclusive licence to Springer Nature B.V. 2021.

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Kluwer Academic Publishers

United States

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Published: 01 October 2021

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Cited By

Khalfi SCaraffini FIacca G(2023)Metaheuristics in the BalanceInternational Journal of Intelligent Systems10.1155/2023/57080852023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/5708085
Palominos POrtega CAlfaro MFuertes GVargas MCamargo MParada VGatica G(2022)Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson ProblemComplexity10.1155/2022/89030052022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8903005

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