MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism

Tsung Che Chiang*, Yung Pin Lai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

49 Citations (Scopus)


In this paper we propose a multiobjective evolutionary algorithm based on MOEA/D [1] for solving multiobjective optimization problems. MOEA/D decomposes a multiobjective optimization problem into many single-objective subproblems. The objective of each subproblem is a weighted aggregation of the original objectives. Using evenly distributed weight vectors on subproblems, solutions to subproblems form a set of well-spread approximated Pareto optimal solutions to the original problem. In MOEA/D, each individual in the population represents the current best solution to one subproblem. Mating selection is carried out in a uniform and static manner. Each individual/subproblem is selected/solved once at each generation, and the mating pool of each individual is determined and fixed based on the distance between weight vectors on the objective space. We propose an adaptive mating selection mechanism for MOEA/D. It classifies subproblems into solved ones and unsolved ones and selects only individuals of unsolved subproblems. Besides, it dynamically adjusts the mating pools of individuals according to their distance on the decision space. The proposed algorithm, MOEA/D-AMS, is compared with two versions of MOEA/D using nine continuous functions. The experimental results confirm the benefits of the adaptive mating selection mechanism.

Original languageEnglish
Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
Number of pages8
Publication statusPublished - 2011
Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States
Duration: 2011 Jun 52011 Jun 8

Publication series

Name2011 IEEE Congress of Evolutionary Computation, CEC 2011


Other2011 IEEE Congress of Evolutionary Computation, CEC 2011
Country/TerritoryUnited States
CityNew Orleans, LA


  • evolutionary algorithm
  • mating pool
  • mating selection
  • multiobjective optimization
  • scalarization
  • subproblem

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science


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