Multi-Colony Ant Optimization Based on Pheromone Fusion Mechanism of Cooperative Game

• Published in: Arabian journal for science and engineering (2011) Vol. 47; no. 2; pp. 1657 - 1674
• Main Authors: Mo, Yadong, You, Xiaoming, Liu, Sheng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022; Springer Nature B.V
• Subjects: Algorithms; Convergence; Engineering; Entropy (Information theory); Game theory; Humanities and Social Sciences; multidisciplinary; Optimization; Pheromones; Research Article-Computer Engineering and Computer Science; Science; Traveling salesman problem; TSP; Pheromone fusion; Information entropy; Ant colony algorithm; Cooperative game
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-021-06033-4

In this paper, we propose a multi-colony ant optimization based on pheromone fusion mechanism of cooperative game (CGMACO) to balance the convergence speed and diversity of the algorithm. Firstly, the heterogeneous multi-colony is composed of ant colony system (ACS) and Max–Min ant system (MMAS), and these two classical colonies coordinate together to improve the solution quality. Secondly, the cooperative game model determines which sub-colonies can interact with each other based on evaluating each union’s payoff, while the pheromone fusion mechanism decides what information can exchange by regulating the pheromone matrix of each subpopulation. Those two methods can greatly diversify the solution of algorithm. In addition, the information entropy is also introduced to control the interaction frequency, which enhances the adaptability of the algorithm. Finally, the experimental results of the large-scale TSP instances show that the improved algorithm can improve the accuracy of the solution without affecting the convergence speed and better than the existing intelligent algorithms.