A novel multi-objective optimization algorithm based on artificial bee colony

  • Authors:

  • Wenping Zou;Yunlong Zhu;Hanning Chen;Hai Shen

  • Affiliations:

  • Chinese Academy of Sciences, Shenyang, & Graduate School of the Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Shenyang, China;Chinese Academy of Sciences, Shenyang, China;Chinese Academy of Sciences, Shenyang, & Graduate School of the Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2011

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Abstract

Multi-objective 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 ZDT1 to ZDT3 and ZDT6, and simulation results show that the proposed approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.