Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient

• Published in: IEEE access Vol. 7; pp. 61628 - 61638
• Main Authors: Zhu, Hongwei, You, Xiaoming, Liu, Sheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; cities-dropout; Clustering algorithms; Correlation; Correlation coefficient; Correlation coefficients; Mathematical model; Multiple colonies; Optimization; Pearson correlation coefficient; Traveling salesman problem; TSP; Unit Distance-Pheromone Operator; Urban areas
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2915673

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 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 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.