An Improved Ant Colony Method for Solving Multi-objective Multi-Traveling Salesman Problem

A multi-travelling salesman, multi-objective technology, applied in the field of ant colony algorithm applied to computer combinatorial optimization, can solve the problems of multi-objective neglect, poor practicability of algorithm operation results, unclear enlightenment information, etc., to achieve good flexibility and scalability, Superior practical value, clear direction effect

Active Publication Date: 2021-09-21
SOUTH CHINA UNIV OF TECH
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  • Abstract
  • Description
  • Claims
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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

Method used

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  • An Improved Ant Colony Method for Solving Multi-objective Multi-Traveling Salesman Problem
  • An Improved Ant Colony Method for Solving Multi-objective Multi-Traveling Salesman Problem
  • An Improved Ant Colony Method for Solving Multi-objective Multi-Traveling Salesman Problem

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with specific examples. Here, the specific example scenario of the multi-objective multi-traveling salesman problem is the courier logistics distribution scenario, that is, there are several couriers in the logistics center who need to complete the express delivery of several points, and it is necessary to satisfy the minimum delivery path length and the path length value of multiple couriers Differences are minimized.

[0041] Such as Figure 1 to Figure 3 As shown, the improved ant colony method for solving the multi-objective multi-traveling salesman problem provided by this embodiment mainly includes steps: 1) ant colony initialization; 2) iterative search and feedback; 3) judging whether the current state satisfies the algorithm termination condition , if yes, terminate and return the final result A(T), if not, return to step 2) and continue to run.

[0042] The specific process steps are explai...

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Abstract

The invention discloses an improved ant colony method for solving the multi-objective multi-traveling salesman problem. By changing the taboo table, each ant in the ant colony can independently construct a feasible solution. Compared with the traditional method of randomly selecting one ant to move each time, multi-ants cooperative construction of feasible solutions has advantages in efficiency and uniformity. In addition, the strategy added by the improved ant colony method also includes random initialization of the pheromone matrix, modification of the state transition formula so that the ants have a certain probability of returning to the warehouse center when moving between distribution points, and additional rounds of pheromone oriented by the optimization of each goal. update etc. The algorithm steps are as follows. After the pheromone matrix is ​​randomly initialized, the ant colony will use the improved state transition formula combined with the round-robin selection algorithm to select the next delivery point successively until a feasible solution is constructed. After the feasible solutions are weighted and scored, this score is used as the benchmark for the amount of pheromone added, and multiple rounds of different amounts of pheromone are added in combination with multiple characteristics of the sub-path.

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 method 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 mechanism ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 胡劲松邓昶博
Owner SOUTH CHINA UNIV OF TECH
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