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The ant colony algorithm is the Italian scholar M.Do rigo, V.Maniezzo and A.Colorni by simulating ant colony foraging behavior of a population - based simulation evolutionary algorithm . Parallelism , distributed , self- organized characteristics , since it was proposed the algorithm as a global search method , ant colony algorithm with positive feedback already in combinatorial optimization , function optimization , system identification , network routing , robot path planning , data mining , and LSI integrated wiring design in areas such as access to a wide range of applications, and achieved good results . This article first ant colony algorithm and ant colony sequence alignment . Existing ant colony algorithm in the choice of path , taking into account the the pheromone and path length of the two factors which led the search process can not simulate real ants , is proposed based on the strength of the pheromone ants swarm algorithm , the algorithm only consider the choice of path pheromone intensity , path length factors when considering updates in the the pheromone intensity initialize pheromone intensity , path selection when considering only the pheromone strength this a factor closer to the real behavior of ants , and tested to verify this algorithm can obtain better search results . Finally, based on the intensity of pheromone ant colony algorithm and a simplified grid model , the ant colony pairwise sequence alignment algorithms , simulation results confirm the effectiveness and feasibility of the algorithm , its performance is higher than the ACA algorithm .
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