Solving multiobjective optimization problems using artificial bee colony algorithm
type: Item
instance of: scholarly article
title: Solving multiobjective optimization problems using artificial bee colony algorithm
zbMATH Open document ID: 1233.90254
DOI: 10.1155/2011/569784
author: Wenping Zou
author: Yunlong Zhu
author: Hanning Chen
author: Beiwei Zhang
published in: Discrete Dynamics in Nature and Society
publication date: 2012-03-13T00:00:00Z
review text: Summary: Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6014510
Wikidata QID: Q56740750
describes a project that uses: MOPSO
describes a project that uses: ABC
describes a project that uses: MOEA/D
describes a project that uses: SPEA2
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2011/569784
OpenAlex ID: W2061428286
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description: scientific article; zbMATH DE number 6014510