Interval Multiobjective Optimization With Memetic Algorithms

One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front w...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 50; H. 8; S. 3444 - 3457
Hauptverfasser:	Sun, Jing, Miao, Zhuang, Gong, Dunwei, Zeng, Xiao-Jun, Li, Junqing, Wang, Gaige
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Desalination Evolutionary algorithm (EA) Evolutionary algorithms interval IP networks Linear programming memetic algorithm (MA) Memetics multiobjective optimization Multiple objective analysis Optimization Pareto optimization Search problems Searching Sun Uncertainty
ISSN:	2168-2267, 2168-2275, 2168-2275
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Abstract	One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.
AbstractList	One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA. One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.
Author	Sun, Jing Miao, Zhuang Zeng, Xiao-Jun Gong, Dunwei Wang, Gaige Li, Junqing
Author_xml	– sequence: 1 givenname: Jing orcidid: 0000-0002-1485-0247 surname: Sun fullname: Sun, Jing email: sunj@hhit.edu.cn organization: School of Science, Huaihai Institute of Technology, Lianyungang, China – sequence: 2 givenname: Zhuang surname: Miao fullname: Miao, Zhuang email: 15162186171@163.com organization: Jiangsu Automation Research Institute of CSIC, Lianyungang, China – sequence: 3 givenname: Dunwei surname: Gong fullname: Gong, Dunwei email: dwgong@vip.163.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 4 givenname: Xiao-Jun orcidid: 0000-0002-2320-2495 surname: Zeng fullname: Zeng, Xiao-Jun email: x.zeng@manchester.ac.uk organization: School of Computer Science, University of Manchester, Manchester, U.K – sequence: 5 givenname: Junqing orcidid: 0000-0001-8440-9601 surname: Li fullname: Li, Junqing email: lijunqing@lcu-cs.com organization: School of Information Science and Engineering, Shandong Normal University, Jinan, China – sequence: 6 givenname: Gaige orcidid: 0000-0002-3295-8972 surname: Wang fullname: Wang, Gaige email: gaigewang@163.com organization: Department of Computer Science and Technology, Ocean University of China, Qingdao
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SubjectTerms	Algorithms Desalination Evolutionary algorithm (EA) Evolutionary algorithms interval IP networks Linear programming memetic algorithm (MA) Memetics multiobjective optimization Multiple objective analysis Optimization Pareto optimization Search problems Searching Sun Uncertainty
Title	Interval Multiobjective Optimization With Memetic Algorithms
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