An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

Shouyong Jiang, Shengxiang Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

148 Citations (Scopus)

Abstract

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

Original languageEnglish
Article number7060668
Pages (from-to)421-437
Number of pages17
JournalIEEE Transactions on Cybernetics
Volume46
Issue number2
DOIs
Publication statusPublished - Feb 2016

Keywords

  • Multiobjective evolutionary algorithm (MOEA)
  • multiobjective evolutionary algorithm based on decomposition (MOEA/D)
  • multiobjective optimization
  • niching
  • test problems

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