Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator
•A crossover operator can enhance the information exchange between particles.•The algorithm directly simulates the decision process of bilevel programming.•The proposed algorithm is a feasible and efficient algorithm for solving HDBLMPPs. In this paper, a hybrid particle swarm optimization with cros...
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| Published in | Knowledge-based systems Vol. 53; pp. 13 - 19 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.11.2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-7051 1872-7409 |
| DOI | 10.1016/j.knosys.2013.07.015 |
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| Abstract | •A crossover operator can enhance the information exchange between particles.•The algorithm directly simulates the decision process of bilevel programming.•The proposed algorithm is a feasible and efficient algorithm for solving HDBLMPPs.
In this paper, a hybrid particle swarm optimization with crossover operator (denoted as C-PSO) is proposed, in which a crossover operator is adopted for enhancing the information exchange between particles to prevent premature convergence of the swarm. The C-PSO algorithm is employed for solving high dimensional bilevel multiobjective programming problem (HDBLMPP) in this study, which performs better than the existing method with respect to the generational distance and has almost the same performance with respect to the spacing. Finally, we use four test problems and a practical application to measure and evaluate the proposed algorithm. Our results indicate that the proposed algorithm is highly competitive with respect to the algorithm representative of the state-of-the-art in high dimensional bilevel multiobjective optimization. |
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| AbstractList | •A crossover operator can enhance the information exchange between particles.•The algorithm directly simulates the decision process of bilevel programming.•The proposed algorithm is a feasible and efficient algorithm for solving HDBLMPPs.
In this paper, a hybrid particle swarm optimization with crossover operator (denoted as C-PSO) is proposed, in which a crossover operator is adopted for enhancing the information exchange between particles to prevent premature convergence of the swarm. The C-PSO algorithm is employed for solving high dimensional bilevel multiobjective programming problem (HDBLMPP) in this study, which performs better than the existing method with respect to the generational distance and has almost the same performance with respect to the spacing. Finally, we use four test problems and a practical application to measure and evaluate the proposed algorithm. Our results indicate that the proposed algorithm is highly competitive with respect to the algorithm representative of the state-of-the-art in high dimensional bilevel multiobjective optimization. In this paper, a hybrid particle swarm optimization with crossover operator (denoted as C-PSO) is proposed, in which a crossover operator is adopted for enhancing the information exchange between particles to prevent premature convergence of the swarm. The C-PSO algorithm is employed for solving high dimensional bilevel multiobjective programming problem (HDBLMPP) in this study, which performs better than the existing method with respect to the generational distance and has almost the same performance with respect to the spacing. Finally, we use four test problems and a practical application to measure and evaluate the proposed algorithm. Our results indicate that the proposed algorithm is highly competitive with respect to the algorithm representative of the state-of-the-art in high dimensional bilevel multiobjective optimization. |
| Author | Guo, Xuning Chen, Zhong Hu, Tiesong Zheng, Yue Zhang, Tao |
| Author_xml | – sequence: 1 givenname: Tao surname: Zhang fullname: Zhang, Tao email: zt_sx981@126.com organization: State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China – sequence: 2 givenname: Tiesong surname: Hu fullname: Hu, Tiesong organization: State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China – sequence: 3 givenname: Xuning surname: Guo fullname: Guo, Xuning email: guoxuning@whu.edu.cn organization: State Key Laboratory of Water Resource and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China – sequence: 4 givenname: Zhong surname: Chen fullname: Chen, Zhong organization: School of Information and Mathematics, Yangtze University, Jingzhou 434023, China – sequence: 5 givenname: Yue surname: Zheng fullname: Zheng, Yue organization: School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China |
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| Snippet | •A crossover operator can enhance the information exchange between particles.•The algorithm directly simulates the decision process of bilevel programming.•The... In this paper, a hybrid particle swarm optimization with crossover operator (denoted as C-PSO) is proposed, in which a crossover operator is adopted for... |
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| SubjectTerms | Algorithms Convergence Crossover operator Crossovers Elite strategy High dimensional bilevel multiobjective programming Knowledge base Mathematical programming Multiple objective Operators Pareto optimal solution Particle swarm optimization Swarm intelligence |
| Title | Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator |
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