Cooperative Multi-ant Colony Pseudo-parallel Optimization Algorithm

摘要

On account of the premature and stagnation of traditional ant colony algorithm, this paper proposes a cooperative multi-ant colony pseudo-parallel optimization algorithm, drawing lessons from the idea of the exclusion model and fitness sharing model of genetic algorithm. The algorithm makes multiple sub-ant colonies run different instance models of ant algorithm independently and concurrently, and realizes the historical experience synthesis of each sub-colony through the interaction of the pheromone, to ensure the guidance and diversity of pheromone distribution. Through the cooperation of the ants in each sub-colony and between sub-colonies, the algorithm achieves the collaborative optimization of ant colony at two levels, thus it improves the ability of optimization and the stability. Algorithm performance test shows that, the algorithm has a better ability of global optimization than the traditional ant colony algorithm.