Cooperative Artificial Bee Colony Algorithm With Multiple Populations for Interval Multiobjective Optimization Problems

In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed int...

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Vydáno v:	IEEE transactions on fuzzy systems Ročník 27; číslo 5; s. 1052 - 1065
Hlavní autoři:	Zhang, Liming, Wang, Saisai, Zhang, Kai, Zhang, Xiuqing, Sun, Zhixue, Zhang, Hao, Chipecane, Miguel Tome, Yao, Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Archive search artificial bee colony cooperative populations Credibility Evolutionary algorithms interval multiobjective optimization Intervals Linear programming Mathematical programming Multiple objective analysis Optimization Optimization methods Pareto optimum Particle swarm optimization Populations Search algorithms Sociology Statistics Swarm intelligence uncertain multicriteria decision-making
ISSN:	1063-6706, 1941-0034
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Abstract	In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed interval multiobjective optimization methods compared to traditional multiobjective optimization methods. Therefore, a novel interval multiobjective optimization method called the Interval Cooperative Multiobjective Artificial Bee Colony Algorithm (ICMOABC) based on multiple populations for multiple objectives and interval credibility is proposed. Interval credibility is selected as the interval dominant method. Interval credibility is easy to combine with multiobjective optimization methods because it can describe the mean and width of intervals without increasing the dimension of the objective functions. The proposed algorithm has M single-objective optimization subpopulations updated by artificial bee colony algorithm, meaning it uses evolutionary resources more efficiently. In order to bring in diversity, the elitist learning strategy is used in the archive. The results of ICMOABC on various benchmark problems sets with different characteristics demonstrate its superior performance compared to some state-of-the-art algorithms.
AbstractList	In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed interval multiobjective optimization methods compared to traditional multiobjective optimization methods. Therefore, a novel interval multiobjective optimization method called the Interval Cooperative Multiobjective Artificial Bee Colony Algorithm (ICMOABC) based on multiple populations for multiple objectives and interval credibility is proposed. Interval credibility is selected as the interval dominant method. Interval credibility is easy to combine with multiobjective optimization methods because it can describe the mean and width of intervals without increasing the dimension of the objective functions. The proposed algorithm has M single-objective optimization subpopulations updated by artificial bee colony algorithm, meaning it uses evolutionary resources more efficiently. In order to bring in diversity, the elitist learning strategy is used in the archive. The results of ICMOABC on various benchmark problems sets with different characteristics demonstrate its superior performance compared to some state-of-the-art algorithms.
Author	Zhang, Xiuqing Zhang, Liming Yao, Jun Wang, Saisai Sun, Zhixue Zhang, Hao Chipecane, Miguel Tome Zhang, Kai
Author_xml	– sequence: 1 givenname: Liming surname: Zhang fullname: Zhang, Liming email: zhangliming@upc.edu.cn organization: Department of Oil Extraction Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 2 givenname: Saisai surname: Wang fullname: Wang, Saisai email: z16020143@s.upc.edu.cn organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 3 givenname: Kai orcidid: 0000-0002-6691-6762 surname: Zhang fullname: Zhang, Kai email: zhangkai@upc.edu.cn organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 4 givenname: Xiuqing surname: Zhang fullname: Zhang, Xiuqing email: 2233737504@qq.com organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 5 givenname: Zhixue surname: Sun fullname: Sun, Zhixue email: upcszx@upc.edu.cn organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 6 givenname: Hao surname: Zhang fullname: Zhang, Hao email: zhlongma008@sina.com organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 7 givenname: Miguel Tome surname: Chipecane fullname: Chipecane, Miguel Tome email: migueltomechipecane@gmail.com organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China – sequence: 8 givenname: Jun surname: Yao fullname: Yao, Jun email: yaojunhdpu@126.com organization: Department of Reservoir Engineering, China University of Petroleum, Qingdao, Shandong, China
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SubjectTerms	Algorithms Archive search artificial bee colony cooperative populations Credibility Evolutionary algorithms interval multiobjective optimization Intervals Linear programming Mathematical programming Multiple objective analysis Optimization Optimization methods Pareto optimum Particle swarm optimization Populations Search algorithms Sociology Statistics Swarm intelligence uncertain multicriteria decision-making
Title	Cooperative Artificial Bee Colony Algorithm With Multiple Populations for Interval Multiobjective Optimization Problems
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