Artificial Immune Ant Colony Algorithm and It Application
Artificial Immune Algorithm has the ability of doing a global searching quickly and stochastically. But it cant make use of enough output information, and hence do a large redundancy repeat searching for the optimal solution, which reduces the efficiency of algorithm. Ant Colony Algorithm is convergent on the optimal path through pheromone accumulation an renewal, and has the ability of parallel processing and global searching. But its initial solution is stochastic, and easy precocious and convergence speed is slow. In this paper we propose a hybrid algorithm based on Artificial Immune Algorithm and Ant Colony Algorithm. It adopts Artificial Immune Algorithm to give pheromone to distribute and makes use of Ant Colony Algorithm to give the optimum solution. The simulation results show that the proposed algorithm is better than the previous algorithms on the convergence speed and ability of searching for approximate global optimum solution for solving Traveling Salesman Problem and function optimization problems.
Yanfang Bu Yuanguo Zhu
Department of Applied Mathematics Nanjing University of Science and Technology Nanjing 210094,Jiangsu,China
国际会议
上海
英文
1890-1895
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）