Paper Info
Title
Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator
Abstract
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.
Year
DOI
Venue
2013
10.1016/j.knosys.2013.07.015
Knowl.-Based Syst.
Keywords
Field
DocType
crossover operator,programming problem,existing method,algorithm representative,practical application,hybrid particle swarm optimization,information exchange,c-pso algorithm,high dimensional bilevel multiobjective,proposed algorithm,generational distance,particle swarm optimization
Swarm behaviour,Computer science,Multi-objective optimization,Multiobjective programming,Artificial intelligence,Operator (computer programming),Particle swarm optimization,Mathematical optimization,Crossover,Premature convergence,Algorithm,Multi-swarm optimization,Machine learning
Journal
Volume
ISSN
Citations 
53,
0950-7051
14
PageRank 
References 
Authors
0.60
28
5
Name
Order
Citations
PageRank
Tao Zhang1271.33
Tiesong Hu2695.31
Xuning Guo3271.33
Zhong Chen4472.84
Yue Zheng5140.60