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research-article

An improved two-archive artificial bee colony algorithm for many-objective optimization

Authors:

Mahamed G.H. Omran,

Jia ZhaoAuthors Info & Claims

Volume 236, Issue C

https://doi.org/10.1016/j.eswa.2023.121281

Published: 01 February 2024 Publication History

Abstract

Artificial bee colony (ABC) algorithm has shown good performance on many optimization problems. However, these problems mainly focus on single-objective and ordinary multi-objective optimization problems (MOPs). For many-objective optimization problems (MaOPs), ABC encounters some difficulties. The selection pressure based on Pareto-dominance degrades severely. It is hard to balance convergence and population diversity. To help ABC solve MaOPs, this paper proposes an improved two-archive many-objective ABC (called MaOABC-TA) algorithm. Inspired by the improved two-archive (Two_Arch2) method, MaOABC-TA uses two archives namely convergence archive (CA) and diversity archive (DA) to promote convergence and diversity. Based on CA and DA, three different search strategies are designed to strengthen convergence or diversity in different search stages. In addition, a new probability selection strategy is proposed to choose solutions with good diversity. To verify the performance of MaOABC-TA, it is compared with 10 many-objective evolutionary algorithms (MaOEAs) and 3 many-objective ABCs on DTLZ and MaF benchmark sets with 3, 5, 8, and 15 objectives. Two performance indicators including inverted generational distance (IGD) and hypervolume (HV) and utilized. Experimental results show that MaOABC-TA is more competitive than the compared algorithms in term of the IGD and HV values.

Highlights

•

Two archives namely convergence archive (CA) and diversity archive (DA) are introduced into ABC.

•

Based on CA and DA, different search strategies are designed to enhance convergence or diversity.

•

A new probability selection strategy is proposed to choose solutions with good diversity.

•

Our approach is compared with 10 popular MaOEAs and 3 many-objective ABCs on two benchmarks.

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Cited By

Zhang BChe AWang Y(2024)Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123011245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123011
Liao FZhang SXiao DWang HZhang H(2024)An Indicator-Based Firefly Algorithm for Many-Objective OptimizationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_19(231-244)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5581-3_19

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An improved two-archive artificial bee colony algorithm for many-objective optimization

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 236, Issue C

Feb 2024

1583 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

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Cited By

Zhang BChe AWang Y(2024)Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123011245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123011
Liao FZhang SXiao DWang HZhang H(2024)An Indicator-Based Firefly Algorithm for Many-Objective OptimizationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_19(231-244)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5581-3_19

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