Public transport network optimization based on a Multi-objective Optimization Problems Evolutionary Algorithm
Considering the benefits of passengers and bus corporations, this paper established a mathematical model of public transport network optimization. A Multi-objective Optimization Problems Evolutionary Algorithm (MOPEA) is presented to solve optimization problems in the public transport network. In this algorithm, the theory of particle system changing from non-equilibrium to equilibrium is used to define the Rank function and Niche function, so all the individuals in the population have chance to participate in the evolving operation such as crossover and mutation to solve the global Pareto optimal solutions of public transport network optimization problems. This algorithm can avoid premature phenomenon of public transport network optimization problems. At the same time, diversity of objective functions is reserved. This algorithm can gain compromise optimal results of conflicting multi-objective optimization problem—Pareto optimal front, and avoid changing multi-objective functions into one objective function by inducting experiential weight coefficient. At last, an example was given and the result showed that this algorithm had more advantages than traditional evolutionary algorithms.