Paper Info
Title
Genetic algorithm based on primal and dual theory for solving multiobjective bilevel linear programming
Abstract
The multiobjective bilevel linear programming (MBLP) is a hierarchical optimization problem involving two levels, and at least one level has multiple objectives. This paper mainly studies a special kind of MBLP with one objective at the lower level. With primal and dual theory, the lower level problem is transformed into a part of constraints of the upper level problem, then by handling the feasible set of the transformed problem, several equivalent problems of MBLP are obtained. Furthermore, by designing three feasible genetic operators, a new genetic algorithm for solving MBLP is presented. The simulations on several designed multiobjective bilevel linear programming problems are made, and the performance of the proposed algorithm is verified by comparing with the existing algorithms. The results show that the proposed algorithm is effective for MBLP.
Year
DOI
Venue
2011
10.1109/CEC.2011.5949668
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
mblp,hierarchical optimization problem,linear programming,equivalent problems,genetic algorithm,multiobjective bilevel linear programming,genetic algorithms,primal theory,dual theory,encoding,algorithm design,program optimization,optimization,linear program,optimization problem,algorithm design and analysis,lead,genetic operator,programming
Mathematical optimization,Algorithm design,Computer science,Algorithm,Feasible region,Artificial intelligence,Operator (computer programming),Linear programming,Optimization problem,Machine learning,Genetic algorithm,Encoding (memory)
Conference
Volume
Issue
ISSN
null
null
Pending
ISBN
Citations 
PageRank 
978-1-4244-7834-7
0
0.34
References 
Authors
9
2
Name
Order
Citations
PageRank
Li-Ping Jia1547.81
Yuping Wang200.34