Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient

Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient

Blog Article

Ant Colony Optimization algorithms have been successfully applied to solve the Traveling Salesman Problem (TSP).However, they still have a tendency to fall into local optima, mainly resulting from poor diversity, especially in those TSPs with a lot of cities.To address this problem, and further obtain a better result in big-scale TSPs, we propose an algorithm called Multiple Colonies Ant Colony Optimization Based on Pearson Correlation Coefficient (PCCACO).To improve the diversity, first, we introduce a novel single colony Ott-Antonsen ansatz truncation of a circular cumulant series termed Unit Distance-Pheromone Operator, which along with two other classic ant populations: Ant Colony System and Max-Min Ant System, make the final whole algorithm.

A Pearson correlation coefficient is further employed to erect multi-colony communication with an adaptive frequency.Besides that, an initialization is Lumbar shape factors extraction influencing the ratio of waist-darts’ volumes for young female applied when the algorithm is stagnant, which helps it to jump out of the local optima.Finally, we render a dropout approach to reduce the running time.The extensive simulations in TSP demonstrate that our algorithm can get a better solution with a reasonable variation.