Multiobjective optimization with ϵ-constrained method for solving real-parameter constrained optimization problems

Jing-Yu JI, Wei-Jie YU*, Yue-Jiao GONG, Jun ZHANG

*Corresponding author for this work

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

18 Citations (Scopus)


This paper develops a novel algorithm to solve real-world constrained optimization problems, which hybridizes multiobjective optimization techniques with an ϵ-constrained method. First, a constrained optimization problem at hand is transformed into a bi-objective optimization problem. By the transformation, the advantage of multiobjective optimization techniques can be utilized in the constrained optimization area to balance population diversity and convergence. Meanwhile, the ϵ-constrained method is applied, which keeps the population evolving toward feasible region of the constrained optimization problem. In our proposed algorithm, the differential evolution is employed as a search engine to create offspring at each generation. Further, different combinations of mutation operators have been developed to improve the search ability and the population convergence at different stages. The performance of our approach is evaluated on 64 benchmark test functions from three popular test suits. Experimental results demonstrate that our proposed approach is capable of obtaining high-quality solutions on the majority of benchmark test functions, when compared with some other state-of-the-art constrained optimization algorithms.

Original languageEnglish
Pages (from-to)15-34
Number of pages20
JournalInformation Sciences
Early online date29 Jul 2018
Publication statusPublished - Oct 2018
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61502544 and 61332002 ).

Publisher Copyright:
© 2018 Elsevier Inc.


  • Constrained optimization problems
  • Differential evolution
  • Multiobjective optimization
  • ϵ-Constrained method


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