Ant Colony Optimization using Genetic Information for TSP
نویسندگان
چکیده
This study proposes an Ant Colony Optimization using Genetic Information (GIACO). The GIACO algorithm combines Ant Colony Optimization (ACO) with Genetic Algorithm (GA). GIACO searches solutions by using the pheromone of ACO and the genetic information of GA. In addition, two kinds of ants coexist: intelligent ant and dull ant. The dull ant is caused by the mutation and cannot trail the pheromone. We apply GIACO to Traveling Salesman Problems (TSPs) and confirm that GIACO obtains more effective results than the conventional ACO and the conventional GA.
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