Multi-ant colony optimization algorithm based on hybrid recommendation mechanism

Traditional ant colony algorithm has the problems of slow convergence speed and easy to fall into local optimum when solving traveling salesman problem. To solve these problems, a multi-ant colony optimization algorithm based on hybrid recommendation mechanism is proposed. Firstly, a heterogeneous multi-ant colony strategy is proposed to balance the convergence and diversity of the algorithm. Secondly, a content-based recommendation strategy is proposed to dynamically divide the traveling salesman problem by self-organizing mapping clustering algorithm, which improves the convergence speed of the algorithm. Thirdly, a collaborative filtering recommendation mechanism based on a multi-attribute decision making model is proposed, including three recommendation strategies: the high-quality solution guidance recommendation strategy based on the convergence factor to improve the convergence of the algorithm; the pheromone fusion recommendation strategy based on the browsing factor to enrich the diversity of the subpopulations; the public path update recommendation strategy based on the population similarity to adaptively regulate the diversity of the algorithm. Finally, when the algorithm stagnates, the association rule-based recommendation strategy is used to help the ant colony jump out of the local optimum. The performance of the improved algorithm is tested on the traveling salesman problem library, and the experimental results show that the proposed algorithm significantly improves the convergence speed and solution accuracy, especially when solving large-scale problems.