A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 23; H. 5; S. 748 - 761
Hauptverfasser:	Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Convergence Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Many-objective evolutionary optimization algorithm Multiple objective analysis nadir point Optimization Pareto optimization Pareto optimum Pareto-optimal subspace Performance degradation Performance enhancement Performance evaluation Sociology Statistics Sun two-stage method
ISSN:	1089-778X, 1941-0026
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Abstract	Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of MaOEAs. Unfortunately, it is not easy to constantly maintain a population of solutions with both convergence and diversity. In this paper, an MaOEA based on two independent stages is proposed for effectively solving many-objective optimization problems (MaOPs), where the convergence and diversity are addressed in two independent and sequential stages. To achieve this, we first propose a nondominated dynamic weight aggregation method by using a genetic algorithm, which is capable of finding the Pareto-optimal solutions for MaOPs with concave, convex, linear and even mixed Pareto front shapes, and then these solutions are employed to learn the Pareto-optimal subspace for the convergence. Afterward, the diversity is addressed by solving a set of single-objective optimization problems with reference lines within the learned Pareto-optimal subspace. To evaluate the performance of the proposed algorithm, a series of experiments are conducted against six state-of-the-art MaOEAs on benchmark test problems. The results show the significantly improved performance of the proposed algorithm over the peer competitors. In addition, the proposed algorithm can focus directly on a chosen part of the objective space if the preference area is known beforehand. Furthermore, the proposed algorithm can also be used to effectively find the nadir points.
AbstractList	Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of MaOEAs. Unfortunately, it is not easy to constantly maintain a population of solutions with both convergence and diversity. In this paper, an MaOEA based on two independent stages is proposed for effectively solving many-objective optimization problems (MaOPs), where the convergence and diversity are addressed in two independent and sequential stages. To achieve this, we first propose a nondominated dynamic weight aggregation method by using a genetic algorithm, which is capable of finding the Pareto-optimal solutions for MaOPs with concave, convex, linear and even mixed Pareto front shapes, and then these solutions are employed to learn the Pareto-optimal subspace for the convergence. Afterward, the diversity is addressed by solving a set of single-objective optimization problems with reference lines within the learned Pareto-optimal subspace. To evaluate the performance of the proposed algorithm, a series of experiments are conducted against six state-of-the-art MaOEAs on benchmark test problems. The results show the significantly improved performance of the proposed algorithm over the peer competitors. In addition, the proposed algorithm can focus directly on a chosen part of the objective space if the preference area is known beforehand. Furthermore, the proposed algorithm can also be used to effectively find the nadir points.
Author	Xue, Bing Zhang, Mengjie Yen, Gary G. Sun, Yanan
Author_xml	– sequence: 1 givenname: Yanan orcidid: 0000-0001-6374-1429 surname: Sun fullname: Sun, Yanan email: yanan.sun@ecs.vuw.ac.nz organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 2 givenname: Bing orcidid: 0000-0002-4865-8026 surname: Xue fullname: Xue, Bing email: bing.xue@ecs.vuw.ac.nz organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 3 givenname: Mengjie orcidid: 0000-0003-4463-9538 surname: Zhang fullname: Zhang, Mengjie email: mengjie.zhang@ecs.vuw.ac.nz organization: School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand – sequence: 4 givenname: Gary G. orcidid: 0000-0001-8851-5348 surname: Yen fullname: Yen, Gary G. email: gyen@okstate.edu organization: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA
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Snippet	Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In...
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SubjectTerms	Convergence Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Many-objective evolutionary optimization algorithm Multiple objective analysis nadir point Optimization Pareto optimization Pareto optimum Pareto-optimal subspace Performance degradation Performance enhancement Performance evaluation Sociology Statistics Sun two-stage method
Title	A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization
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