Ant colony optimization method by introducing curiosity factor

Abstract

The invention relates to an ant colony optimization method by introducing a curiosity factor. The conventional algorithm is easy to stagnate. The method comprises the following steps of: initializing the number and evaporation coefficient of ants and pheromone on each side, placing m ants in n cities randomly, allowing the ants to transfer to a city j randomly, inserting j into a tabu list, deleting the city j from allowed<k>, and updating pheromone concentration in a path through which the kth ant passes partially; calculating the overall path length of each ant, updating the found shortest path, adjusting the pheromone concentration in the path by using a taxation operator, and obtaining the pheromone concentration Tau<ij><*>(t) after taxation; and if the condition that Nc is more than a set value or all ants select the same path is met, finishing the algorithm, and outputting the optimized path with overall optimization at the same time. Through the method, a solution space can be more globally searched.

Description

technical field The invention belongs to the field of information and control technology, relates to automation technology, in particular to an improved ant colony optimization method. Background technique The ant colony optimization algorithm was first proposed by Italian scholars M.Dorigo, V.Maniezzo, A Colorni and others in the early 1990s, and successfully solved combinatorial optimization problems such as traveling salesman (TSP). Ant colony algorithm can not only intelligent search and global optimization, but also has the characteristics of robustness (robustness), positive feedback, distributed computing, and easy combination with other algorithms. Therefore, the advent of ant colony algorithm provides a powerful tool for solving complex optimization problems in many fields. However, the ant colony algorithm uses a random selection strategy in the process of constructing the solution. This selection strategy makes the evolution rate slower and prone to stagnation. ...

AI Technical Summary

Problems solved by technology

However, the ant colony algorithm uses a random selection strategy in the process of constructing the solution. This selection strategy makes the evolution rate slower and prone to stagnation. Searching the solution space further is not conducive to finding better solutions

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