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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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 47; H. 9; S. 2678 - 2688
Hauptverfasser:	Sanyou Zeng, Ruwang Jiao, Changhe Li, Xi Li, Alkasassbeh, Jawdat S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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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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.
AbstractList	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.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. 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.
Author	Sanyou Zeng Changhe Li Xi Li Ruwang Jiao Alkasassbeh, Jawdat S.
Author_xml	– sequence: 1 surname: Sanyou Zeng fullname: Sanyou Zeng email: sanyouzeng@gmail.com organization: Sch. of Mech. Eng. & Electron. Inf., China Univ. of Geosci., Wuhan, China – sequence: 2 surname: Ruwang Jiao fullname: Ruwang Jiao email: ruwangjiao@gmail.com organization: Sch. of Mech. Eng. & Electron. Inf., China Univ. of Geosci., Wuhan, China – sequence: 3 surname: Changhe Li fullname: Changhe Li email: changhe.lw@gmail.com organization: Sch. of Autom., China Univ. of Geosci., Wuhan, China – sequence: 4 surname: Xi Li fullname: Xi Li email: lixi_sjz@foxmail.com organization: Sch. of Inf. Eng., Hebei GEO Univ., Shijiazhuang, China – sequence: 5 givenname: Jawdat S. surname: Alkasassbeh fullname: Alkasassbeh, Jawdat S. email: jawdat1983@yahoo.com organization: Sch. of Mech. Eng. & Electron. Inf., China Univ. of Geosci., Wuhan, China
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SubjectTerms	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
Title	A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization
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