An Improved Intelligent Ant Colony Optimization Based on Genetic Algorithm

For the conflict of ant colony algorithm for accelerating convergence and premature stagnation phenomenon,according to genetic algorithm crossover operator,mutation operator and particle extreme value of particle swarm algorithm,we studied and improved the ant colony algorithm,to achieve good balance between accelerating convergence and preventing premature stagnation phenomenon.Based on this algorithm,for TSP problem,the current solution is made crossover operation with individual extreme and global extreme respectively.The solution is the new position information produced.The experiment results of 50 urban problems show that it is better than general ant colony algorithm in convergence speed and stability,and suitable for solving large-scale problems.