An improved ant colony optimization algorithm based on dynamically adjusting ant number
ROBIO(2012)
摘要
The ant colony algorithm is a mature and effective method to solve the problem of optimizing shortest path, which is one of the key technologies for robot navigation and path planning. But the algorithm often fails into precocity easily and can't get the global best result. This paper proposes an improved ant colony optimization algorithm by dynamically adjusting ant number. The main idea of this algorithm is that only the part of the ants passing the shorter path is allowed to release pheromone and update the total ant number randomly or fixedly in algorithm iterative process. So, the improved algorithm can increase the randomness in the search and improve global search ability. To verify the performance of this algorithm, this paper uses the improved algorithm to solve Chinese Traveling Salesmen Problem. The simulation results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm is easier to find the optimal solution, and its optimization ability is stronger.
更多查看译文
关键词
ctsp,ant colony optimization algorithm,algorithm iterative process,ant colony optimisation,travelling salesman problems,mobile robots,ant number,path planning,navigation,pheromone,ant colony algorithm,chinese traveling salesmen problem,global search ability,robot navigation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络