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Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

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  • Wenping Zou
  • Yunlong Zhu
  • Hanning Chen
  • Beiwei Zhang

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.

Suggested Citation

  • Wenping Zou & Yunlong Zhu & Hanning Chen & Beiwei Zhang, 2011. "Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2011, pages 1-37, November.
    • Handle: RePEc:hin:jnddns:569784
    DOI: 10.1155/2011/569784
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    Cited by:

    1. Nien-Che Yang & Danish Mehmood & Kai-You Lai, 2021. "Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems," Mathematics, MDPI, vol. 9(24), pages 1-19, December.

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