An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed f...

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Bibliographic Details
Published in:Journal of Applied Mathematics Vol. 2012; no. 2012; pp. 1441 - 1453-491
Main Authors: Guo, Xuning, Zheng, Yue, Hu, Tiesong, Zhang, Tao
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Limiteds 01.01.2012
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Approximation
Heuristic
Linear programming
Mathematical models
Optimization
Pareto optimality
Pareto optimum
Programming
Strategy
Studies
ISSN:1110-757X, 1687-0042
Online Access:Get full text
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Summary:An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.
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ISSN:1110-757X
1687-0042
DOI:10.1155/2012/626717