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Discrete Dynamics in Nature and Society
Volume 2011, Article ID 569784, 37 pages
http://dx.doi.org/10.1155/2011/569784
Research Article

Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

1Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2Graduate School of the Chinese Academy of Sciences, Beijing 100039, China

Received 10 May 2011; Revised 9 August 2011; Accepted 23 August 2011

Academic Editor: Binggen Zhang

Copyright © 2011 Wenping Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.