Solving traveling salesman problem with improved MIMIC algorithm
Jing Xiao · Jisuanji gongcheng yu sheji · 2010
To solve the traveling salesman problem in the field of combinatorial optimization effectively,the improved mutual information maximization for input clustering algorithm is extended from binary code to decimal code,which is a kind of the dependency-bivariate estimation of distribution algorithms.It sets up a probabilistic model of the distribution of cities in TSP.This model describes the distribution of traveling paths and then guides to sample randomly.Through such process,the population gets constant evolution and finally achieves the aim of finding the best traveling path.The experimental results show that the improved MIMIC algorithm is effective for TSP problem.