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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Bibliographic Details
Published in:Knowledge-based systems Vol. 53; pp. 13 - 19
Main Authors: Zhang, Tao, Hu, Tiesong, Guo, Xuning, Chen, Zhong, Zheng, Yue
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2013
Subjects:
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
Pareto optimal solution
High dimensional bilevel multiobjective programming
Elite strategy
Particle swarm optimization
Crossover operator
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:•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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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2013.07.015