Ant colony algorithm optimization method based on reverse learning

Abstract

The invention relates to an ant colony algorithm optimization method based on reverse learning, which is used for solving a traveling salesman problem. The improvement of the algorithm mainly comprises the following points: 1, after an initial path is solved, reversing the serial number of each city in the initial path, and constructing a reverse path; 2, sorting the initial paths and the reversepaths from small to large according to the lengths, and taking part of the paths to form a group of new paths; and 3, setting an iteration threshold value, and if the current iteration frequency doesnot reach the iteration threshold value, carrying out pheromone updating on a new group of paths; otherwise, performing pheromone updating on the initial path. The pheromone updating aspect of the basic ant colony algorithm is improved, in the early stage of iteration, reverse learning is introduced to construct a reverse path, and participates in pheromone updating, so that the search range of ants is expanded, the ants are prevented from falling into local extremum, and exploration and development of the understanding space are balanced.

Description

technical field [0001] The invention belongs to the technical field of computers, and relates to an ant colony algorithm optimization method based on reverse learning. Background technique [0002] With the development of science and technology, optimization methods have been widely used in artificial intelligence, electronic science, transportation, public management and other fields. Inspired by nature, many experts and scholars at home and abroad have proposed a series of optimization algorithms by simulating natural phenomena and biological behaviors, including: artificial potential field method, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm and ant colony algorithm. Among them, the artificial potential field method lacks global information and is prone to local extremum; the simulated annealing algorithm has a slow convergence speed, and its performance is more sensitive to parameters; the genetic algorithm has a large amount of...

AI Technical Summary

Problems solved by technology

Among them, the artificial potential field method lacks global information and is prone to local extremum; the simulated annealing algorithm has a slow convergence speed, and its performance is more sensitive to parameters; the genetic algorithm has a large amount of calculation and the search speed is slow; the particle swarm algorithm is prone to premature convergence , the local optimization ability is poor; the ant colony algorithm has defects such as too long search time, easy to fall into local optimal solution, etc.

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