Parallel Ant Colonies for Combinatorial Optimization Problems
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Information Exchange in Multi Colony Ant Algorithms
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
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
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Ant Colony System is a new meta heuristics algorithms to solve hard combinatorial optimization problems. It is a population based approach that uses exploitation of positive feedback as well as greedy search. In this paper, we propose a multi colony interaction ant model that achieves positive·negative interaction through an elite strategy divided by intensification strategy and diversification strategy to improve the performance of original ACS. Positive interaction makes agents belonging to other colony to select the high frequency of the visit of edge, and negative interaction makes to escape the selection of relevant edge. And, we compares with original ACS method for the performance. This multi colony interaction ant model can be applied effectively in occasion that problem regions are big and complex, parallel processing is available, and can improve the performance ACS model.