Parameters Sensitive Analyses for Using Genetic Algorithm to Solve Continuous Network Design Problems

Meng Xu · Jin Tan Yang · Ziyou Gao

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Transportation Planning and Optimization Metaheuristic Optimization Algorithms Research Traffic control and management

Abstract

In this paper, the authors will discuss the parameters' settings using genetic algorithm to solve continuous network design problems (CNDP). The CNDP is formulated as a bi-level programming model. A sensitive analysis method, one-at-a-time design, is used to analyze the effects of the parameters. The analysis demonstrated that the setting of population size has clear effects on the solution; the effects of crossover probability and mutation probability are less than the effects of their combinations. The fields of these parameters are also given in this paper, which avoid to set them blindly in algorithm designs.

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References

1978Statistics for experimenters. An introduction to design, data analysis, and model building.1998Models and algorithms for road network design: a review and some new developments 1999Handbook of simulation: Principles, methodology, advances, applications, and practice 1979Statistics for Experiments. An Introduction to Design, Data Analysis and Model Building.1987Equilibrium Decomposed Optimization: A Heuristic for the Continuous Equilibrium Network Design Problem 2005Optimal Transit Route Network Design Problem with Variable Transit Demand: Genetic Algorithm Approach 2000Genetic-Algorithms-Based Approach for Bilevel Programming Models 1992A Simulated Annealing Approach to the Network Design Problem with Variational Inequality Constraints

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