Evolutionary Path Control Strategy for Solving Many-Objective Optimization Problem

Proteek Chandan ROY, Md. Monirul ISLAM, Kazuyuki MURASE, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

45 Citations (Scopus)


The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaOPs. The central component of our algorithm is the use of a reference vector that helps simultaneously minimizing all the objectives of an MaOP. In doing so, EPCS employs a new fitness assignment strategy for survival selection. This strategy consists of two procedures and our algorithm applies them sequentially. It encourages a population of solutions to follow a certain path reaching toward the Pareto optimal front. The essence of our strategy is that it reduces the number of nondominated solutions to increase selection pressure in evolution. Furthermore, unlike previous work, EPCS is able to apply the classical Pareto-dominance relation with the new fitness assignment strategy. Our algorithm has been tested extensively on several scalable test problems, namely five DTLZ problems with 5 to 40 objectives and six WFG problems with 2 to 13 objectives. Furthermore, the algorithm has been tested on six CEC09 problems having 2 or 3 objectives. The experimental results show that EPCS is capable of finding better solutions compared to other existing algorithms for problems with an increasing number of objectives. © 2013 IEEE.
Original languageEnglish
Article number6860317
Pages (from-to)702-715
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number4
Early online date18 Jul 2014
Publication statusPublished - Apr 2015
Externally publishedYes


  • Evolutionary algorithm
  • multiobjective optimization
  • path control strategy
  • reference vector


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