title: A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization.
abstract: A novel multiobjective technique is proposed for solving constrained optimization problems (COPs) in this paper. The method highlights three different perspectives: 1) a COP is converted into an equivalent dynamic constrained multiobjective optimization problem (DCMOP) with three objectives: a) the original objective; b) a constraint-violation objective; and c) a niche-count objective; 2) a method of gradually reducing the constraint boundary aims to handle the constraint difficulty; and 3) a method of gradually reducing the niche size aims to handle the multimodal difficulty. A general framework of the design of dynamic constrained multiobjective evolutionary algorithms is proposed for solving DCMOPs. Three popular types of multiobjective evolutionary algorithms, i.e., Pareto ranking-based, decomposition-based, and hype-volume indicator-based, are employed to instantiate the framework. The three instantiations are tested on two benchmark suites. Experimental results show that they perform better than or competitive to a set of state-of-the-art constraint optimizers, especially on problems with a large number of dimensions.
authors.forename: Sanyou
authors.initials: S
authors.lastname: Zeng
authors.forename: Ruwang
authors.initials: R
authors.lastname: Jiao
authors.forename: Changhe
authors.initials: C
authors.lastname: Li
authors.forename: Xi
authors.initials: X
authors.lastname: Li
authors.forename: Jawdat S
authors.initials: JS
authors.lastname: Alkasassbeh