A combined constraint handling framework: an empirical study

Chengyong Si, Junjie Hu, Tian Lan, Lei Wang, Qidi Wu

Research output: Contribution to journalJournal articleResearchpeer-review


This paper presents a new combined constraint handling framework (CCHF) for solving constrained optimization problems (COPs). The framework combines promising aspects of different constraint handling techniques (CHTs) in different situations with consideration of problem characteristics. In order to realize the framework, the features of two popular used CHTs (i.e., Deb’s feasibility-based rule and multi-objective optimization technique) are firstly studied based on their relationship with penalty function method. And then, a general relationship between problem characteristics and CHTs in different situations (i.e., infeasible situation, semi-feasible situation, and feasible situation) is empirically obtained. Finally, CCHF is proposed based on the corresponding relationship. Also, for the first time, this paper demonstrates that multi-objective optimization technique essentially can be expressed in the form of penalty function method. As CCHF combines promising aspects of different CHTs, it shows good performance on the 22 well-known benchmark test functions. In general, it is comparable to the other four differential evolution-based approaches and five dynamic or ensemble state-of-the-art approaches for constrained optimization.
Original languageEnglish
JournalMemetic Computing
Issue number1
Pages (from-to)69-88
Publication statusPublished - 2017


  • Combined constraint handling framework (CCHF)
  • Constrained optimization
  • Constraint handling techniques
  • Differential evolution
  • Ranking methods

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