蚁群算法是由M. Dorigo等人提出的一种模拟进化算法，已用于求解旅

The ant colony algorithm is a simulated evolutionary algorithm proposed by M. Dorigo and others. It has been used to solve combined optimization problems such as the traveling salesman TSP problem, job-shop scheduling, and secondary planning, but it also has a long search time and is easy to appear Phenomenon such as stagnation. In this paper, by adjusting the volatilization concentration of ant pheromone to improve the adaptability of the optimization process, it is used for the optimization decision-making of multiple process routes of parts. The optimization process is as follows.

The ant colony algorithm is made up of M. A simulated evolutionary algorithm proposed by Dorigo et al. has been used to solve the combination optimization problems of traveler TSP problem, job-shop scheduling, secondary planning, etc., but there are also long search times, easy to appear stagnation and so on. In this paper, by adjusting the volatile concentration of ant information to improve the adaptability of the optimization process, for the part multi-route optimization decision-making, the optimization process is as follows.

Ant colony algorithm (ACO) is a simulated evolutionary algorithm proposed by M. Dorigo. It has been used to solve traveling salesman TSP problem, job shop scheduling, quadratic programming and other combinatorial optimization problems. However, it has a long search time and is prone to stagnation. In this paper, the ant pheromone volatilization concentration is adjusted to improve the adaptability of the optimization process, which is used to optimize the multi process route of parts. The optimization process is as follows.<br>