A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization

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...

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Vydáno v:IEEE transactions on cybernetics Ročník 47; číslo 9; s. 2678 - 2688
Hlavní autoři: Sanyou Zeng, Ruwang Jiao, Changhe Li, Xi Li, Alkasassbeh, Jawdat S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Constrained optimization
Constraints
dynamic multiobjective optimization
Evolutionary algorithms
Evolutionary computation
Evolutionary design method
Genetic algorithms
Heuristic algorithms
Linear programming
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Sociology
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí: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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2647742