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Many decision-making problems in traffic and logistics network system planning can be formulated as bilevel programming models, which are intrinsically nonconvex and existing algorithms are either difficult to obtain the global optimal solution or to solve large scale problems with the complexity and efficiency issues. In this paper, based on the idea of evolution game and multi-objective optimization nondominated sort, a hierarchical particle swarm algorithm for the model has been designed, by the interaction of two particle swarm algorithm iterations to simulate the interaction between policy-makers during the gmne searching, and to obtain the optimal solution of bilevel programming problems, in order to maximize the interests of all parties. Finally, the application of the model and its algorithm are illustrated with test function. [ABSTRACT FROM AUTHOR]

Abstract: The particle swarm optimization (PSO) algorithm is applied to the problem of MOSFET parameter extraction for the first time. It is shown to perform significantly better than the genetic algorithm (GA). Several modifications of the basic PSO algorithm have been implemented: (a) Hierarchical PSO (HPSO) in which particles are hierarchically arranged and influenced by the positions of the local and global leaders, (b) memory loss operation due to which a particle forgets its past best position, (c) intensive local search in which the solution space around the global leader is searched with a high resolution, and (d) adaptive inertia which causes the inertia of the particles to change adaptively, depending on the fitness of the population. It is demonstrated that the above features improve the performance of the basic PSO algorithm both for the MOSFET parameter extraction problem and for benchmark functions. [Copyright &y& Elsevier]