Research On The Ant Colony Algorithm Based On Pheromone Intensity And Its Application

A population-based simulated evolutionary algorithm called ant colony algorithm (ACA for short) was proposed by Italian researchers M .D rigo, V.Maniezzo and A .Colorni. As a global searching approach, ACA has some characteristic, such as positive feedback, distributing, paralleling, self-organizing, etc, and since it was introduced, the algorithm has been widely applied to the fields of combinatorial optimization, function optimization, system identification, network routing, path planning of robot, data mining and premises distribution of large scale integrated circuit etc, and good effects of application are gained.This paper focuses on the ACA and its application on the Pairwise Alignment. In nowadays, many of the ant colony algorithms have a same characteristic. When select the next path, they refer to the information of both pheromone and distance. This characteristic can't simulate the real ant very well. Inspired by this phenomenon, an ant colony algorithm based on the intensity of pheromone (OPACO) was proposed, which only depend on the intensity of pheromone to select the next city. This algorithm uses the information of distance when initialize and update the pheromone, and it introduced a strategy of dynamic pheromone updating. This algorithm can well simulate the real ant colony more by these strategies. Then, an ant Pairwise Alignment was proposed based on the proposed OPACO and Simplified Grid. Simulated experiments for the sequence alignment show the validity and the feasibility of the proposed algorithm, and that the algorithm performs better than the ant colony system (ACS).