An Improved Genetic Evolutionary Algorithm for Commuter Route Optimization

Abstract

Commuter route problem is a very wide range of intelligent optimization problems in the field of public transport research. Solving route optimization problems is of great significance. Genetic algorithm (GA) is one of the effective methods to solve this kind of problem. The standard genetic algorithm has some limitations. In order to solve the problems that the standard genetic algorithm is easy to be premature and easy to fall into the local optimal solution, an improved genetic evolutionary algorithm(IGEA) is constructed by using adaptive neighborhood method to construct initial population, adaptive crossover mutation probability function and evolutionary reversal etc. to improve standard GA, which improves the quality of the population, enhances the local search ability of genetic algorithm and increases the probability that the offspring will inherit the high quality gene from the father. The simulation results show that the IGEA has better ability to search the optimal solution, the convergence effect is better and the calculation result is more stable.