Dynamically induced clustering ant colony algorithm based on a coevolutionary chain

Abstract

To improve the accuracy and stability of the ant colony algorithm, a dynamically induced clustering ant colony algorithm based on a coevolutionary chain is proposed. First, the pheromone distribution left by ants in small data clusters are divided based on density is used to induce subsequent ants to make selections, thus balancing the convergence speed and solution accuracy. Second, when the algorithm stalls, the coevolutionary chain increases the diversity and stability of the algorithm through population coevolution and dimensionality reduction on chain loops, assisting helping the algorithm get rid of the local optimum. Simulation experiments and rank-sum test analysis showed that the improved ant colony algorithm can effectively balance convergence speed and solution accuracy, and it has better stability.