A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems

被引:80
作者
Wan, Zhongping [1 ]
Wang, Guangmin [2 ]
Sun, Bin [1 ]
机构
[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China
[2] China Univ Geosci, Sch Econ & Management, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonlinear bilevel programming problems; Hybrid intelligent algorithm; Particle swarm optimization; Chaos search technique; NEURAL-NETWORK APPROACH; STACKELBERG-SOLUTIONS; GENETIC ALGORITHM; MODEL;
D O I
10.1016/j.swevo.2012.08.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a hybrid intelligent algorithm by combining the particle swarm optimization (PSO) with chaos searching technique (CST) is presented for solving nonlinear bilevel programming problems. The bilevel programming is transformed into a single level programming problem by use of the KKT conditions of the lower level problem. Then, the hybrid intelligent algorithm is proposed to solve the transformed problem. Our approach embeds the CST into PSO. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. Secondly, an optimization problem is solved by CST to judge whether the particle is feasible or not. In each iteration, all the feasible particles are ranked in ascending order. Particles in the front of list are updated by PSO, while particles in the end of list are updated by CST. The CST used here is not only to enhance the particles but also to improve the diversity of the particle swarm so as to avoid PSO trapping the local optima. Finally, the hybrid intelligent algorithm is commented by illustrating the numerical results on several benchmark problems from the references. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:26 / 32
页数:7
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