Future Generation Computer Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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To avoid premature convergence and stagnation problems in classical ant colony system, a novel multibehavior based multi-colony ant algorithm (MBMCAA) is proposed. The ant colony is divided into several sub-colonies; the subcolonies have their own population evolved independently and in parallel according to four different behavior options, and update their local pheromone and global pheromone level respectively according to immigrant operator. This parallel and cooperating optimization scheme by using different behavioral characteristics and inter-colonies migration strategies can help the algorithm skip from local optimum effectively. The experimental results for TSP show the validity of this algorithm.