MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives

  • Authors:

  • Yan-Yan Tan;Yong-Chang Jiao;Hong Li;Xin-Kuan Wang

  • Affiliations:

  • National Key Laboratory of Antennas and Microwave Technology, Xidian University, Xi'an, Shaanxi 710071, PR China;National Key Laboratory of Antennas and Microwave Technology, Xidian University, Xi'an, Shaanxi 710071, PR China;School of Science, Xidian University, Xi'an, Shaanxi 710071, PR China;National Key Laboratory of Antennas and Microwave Technology, Xidian University, Xi'an, Shaanxi 710071, PR China

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2013

Quantified Score

Hi-index 0.01

Visualization

Abstract

To extend multiobjective evolutionary algorithm based on decomposition (MOEA/D) in higher dimensional objective spaces, this paper proposes a new version of MOEA/D with uniform design, named the uniform design multiobjective evolutionary algorithm based on decomposition (UMOEA/D), and compares the proposed algorithm with MOEA/D and NSGA-II on some scalable test problems with three to five objectives. UMOEA/D adopts the uniform design method to set the aggregation coefficient vectors of the subproblems. Compared with MOEA/D, distribution of the coefficient vectors is more uniform over the design space, and the population size neither increases nonlinearly with the number of objectives nor considers a formulaic setting. The experimental results indicate that UMOEA/D outperforms MOEA/D and NSGA-II on almost all these many-objective test instances, especially on problems with higher dimensional objectives and complicated Pareto set shapes. Experimental results also show that UMOEA/D runs faster than NSGA-II for the problems used in this paper. In additional, the results obtained are very competitive when comparing UMOEA/D with some other algorithm on the multiobjective knapsack problems.