A co-evolutionary multi-objective optimization algorithm based on direction vectors

  • Authors:

  • L. C. Jiao;Handing Wang;R. H. Shang;F. Liu

  • Affiliations:

  • Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Electronic Engineering, Xi'an 710071, China;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Electronic Engineering, Xi'an 710071, China;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Electronic Engineering, Xi'an 710071, China;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Electronic Engineering, Xi'an 710071, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

Most real world multi-objective problems (MOPs) have a complicated solution space. Facing such problems, a direction vectors based co-evolutionary multi-objective optimization algorithm (DVCMOA) that introduces the decomposition idea from MOEA/D to co-evolutionary algorithms is proposed in this paper. It is novel in the sense that DVCMOA applies the concept of direction vectors to co-evolutionary algorithms. DVCMOA first divides the entire population into several subpopulations on the basis of the initial direction vectors in the objective space. Then, it solves MOPs through the co-evolutionary interaction among the subpopulations in which individuals are classified according to their direction vectors. Finally, it explores the less developed regions to maintain the relatively uniform distribution of the solution space. In this way, DVCMOA has advantages in convergence, diversity and uniform distribution of the non-dominated solution set, which are explained through comparison with other state-of-the-art multi-objective optimization evolutionary algorithms (MOEAs) in this paper. DVCMOA is shown to be effective on 6 multi-objective 0-1 knapsack problems.