Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization | Zendy

Tao Zhang | Zendy; Xiaohui Lei | Zendy; Jiawei Chen | Zendy; Zhongping Wan | Zendy; Xuning Guo | Zendy

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Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization

Author(s) -

Tao Zhang,

Xiaohui Lei,

Jiawei Chen,

Zhongping Wan,

Xuning Guo

Publication year - 2012

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2012/102482

Subject(s) - mathematical optimization , mathematics , dimension (graph theory) , particle swarm optimization , convergence (economics) , multi objective optimization , multi swarm optimization , premature convergence , bilevel optimization , measure (data warehouse) , optimization problem , computer science , data mining , pure mathematics , economics , economic growth

An elite quantum behaved particle swarm optimization (EQPSO) algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP) in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension BLMPPs, as well as attempt to solve the BLMPPs whose theoretical Pareto optimal front is not known. The experimental results show that the proposed algorithm is a feasible and efficient method for solving BLMPPs

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