A parallel 2-opt algorithm for the traveling salesman problem
Future Generation Computer Systems - Special issue: massive parallel computing
Future Generation Computer Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
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To enhance the diversity of search space, an improved version of Ant Colony Optimization (ACO), Mean-Contribution Ant System (MCAS) which is derived from Max-Min Ant System (MMAS), is presented in this paper. A new contribution function introduced in MCAS is used to improve the selection strategy of ants and the mechanism “pheromone trails smooth” mentioned by MMAS. Influenced by the improvements, the diversity of search space can be enhanced, which leads to better results. A series of benchmark Traveling Salesman Problems (TSPs) were utilized to test the performances of MCAS and MMAS respectively. The experiment results indicate that MCAS can outperform MMAS in most cases.