Improved ant colony algorithm for solving multi-objective multi-traveler problem

Abstract

The present invention discloses an improved ant colony algorithm for solving the multi-objective multi-traveler problem. By changing the taboo table, a feasible solution can be constructed separatelyfor each ant of the ant colony. Compared with the traditional method for randomly selecting an ant for movement each time and constructing a feasible solution through multi-ant collaboration, the improved ant colony algorithm disclosed by the present invention has superior efficiency and uniformity; and in addition, added strategies of the improved ant colony algorithm further comprise randomly initializing the pheromone matrix, modifying a state transition formula to make ants return to the warehouse center with a certain probability when the ants move between cities, extra adding rounds of pheromone updates with each target optimization as an orientation, and the like. The algorithm comprises the following steps that: after randomly initializing the pheromone matrix, the ant colony willuse the improved state transition formula combined with the rotation selection algorithm to successively select the next city until a feasible solution is constructed; and after weighted scoring the feasible solution, the score is taken as a reference for the pheromone addition amount, and in combination with multiple features of the sub-path, pheromone addition with multiple rounds and differentamounts is performed.

Description

technical field [0001] The invention relates to the technical field of ant colony algorithm applied to computer combination optimization, in particular to an improved ant colony algorithm for solving multi-objective multi-traveling salesman problems. Background technique [0002] Ant colony algorithm uses pheromone matrix, combined with heuristic information to guide tabu search, and finally converges to the optimal solution with the help of pheromone positive feedback mechanism. It is a kind of iterative search algorithm with fast convergence and excellent feasible solution. The algorithm draws on the foraging process of ant colony, so that the ants in the algorithm release pheromones on the path and move along the path with more pheromones with a high probability. Because the path through which the ants pass through the optimal solution releases more pheromone, the path with more pheromone will in turn attract more ants to choose the path, and the positive feedback mechani...

AI Technical Summary

Problems solved by technology

However, due to the lack of guidance information of these algorithms, the practicability of the algorithm operation results is poor; and for this problem, the ant colony algorithm with heuristic information is prone to unclear heuristic information, and the situation of multi-objective consideration and loss; therefore, we need to improve the ant colony algorithm so that The algorithm can be well adapted to the new multi-objective multi-traveling salesman problem model

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