Cooperative multi-ant colony pseudo-parallel optimization algorithm

• Published in: 2010 International Conference on Information and Automation pp. 1269 - 1274
• Main Authors: Liqiang Liu, Yang Song, Yuntao Dai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Algorithm design and analysis; Ant colony algorithm; Ant colony optimization; Automation; Cities and towns; Collaboration; Cooperative; Educational institutions; Genetic algorithms; Heuristic algorithms; Multi-ant colony; Optimization; Testing; Traveling salesman problems
• ISBN: 1424457017; 9781424457014
• DOI: 10.1109/ICINFA.2010.5512118

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.