蚁群遗传混合算法在求解旅行商问题上的应用

针对在求解旅行商问题时,蚁群算法易陷入局部最优,而遗传算法收敛速度慢等问题,将蚁群与遗传算法相结合:把蚁群算法每次迭代的结果作为遗传算法的初始种群,并且用遗传算法寻优结果更新蚁群算法的信息素。在用遗传算法处理问题的阶段,引入了两种新的交叉算子,并且提出混合交叉算子的新思想,算法的后期使用贪心搜索和2-opt局部优化算法,成功的避免了算法过早陷入局部最优解的问题,加快了算法的收敛速度。通过仿真,本算法与其他算法进行对比,寻优路径长度明显降低,在求解效率和求解质量上都有更好的效果。

The Application of Ant Colony and Genetic Hybrid Algorithm on TSP

In solving the traveling salesman problem,ant colony algorithm is easy to fall into local optimum,and genetic algorithm converges slowly.To overcome the problem,the Ant Colony Algorithm and the Genetic Algorithm were combined which uses the results of each iteration of the ant colony algorithm as the initial population of the genetic algorithms,and the pheromone of ant colony algorithm is updated by the optimization result of genetic algorithm.In the process of genetic algorithm,two new crossover operators were introduced and a new hybrid crossover operator scheme was proposed.To avoid the proposed algorithm falling into the local optimal solution and accelerate the convergence speed,the greedy search algorithm and the 2-opt local optimization algorithm are adopted in proposed hybrid algorithm.The simulation results show that the route path length is significantly reduced,and the proposed algorithm is more effective,compared with others algorithms.