Optimization and Improvement of Genetic Algorithms Solving Traveling Salesman Problem
Traveling Salesman Problem (TSP) is a typical NP-complete problem, of which the search space increases with the number of cities. Genetic Algorithm (GA) is an efficient optimization algorithm characterized with explicit parallelism and robustness, applicable to TSP. In this paper, we compare the performance of the existing GAs in searching the solution for TSP and find a superior combination of crossover and mutation method. Then, the improvements in the Cycle Crossover and Greedy Cross-Cycle Crossover are proposed. Finallyl experimental results show that the new Cycle Crossover and Greedy Crossover algorithms perform much better than the original ones.
TSP Genetic Algorithms Crossover Operator Cycle Crossover Greedy Crossover
Liping Zhang Min Yao Nenggan Zheng
College of Computer Science, Zhejiang University Hangzhou 310027, China
国际会议
2009图像分析与信号处理国际会议(2009 International Conference on Image Analysis and Signal Processing)
浙江台州
英文
327-332
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）