Ant Colony Optimization Algorithm And Applications

Ant colony algorithm (ACA) was introduced by M.Dorigo and colleagues as a novel nature-inspired metaheuristic based on the phenomenon of real ants foraging behavior, which belongs to the class of swarm intelligence. ACA inspires the phenomenon that ants cooperate to find the shortest routing path by means of indirect communication using a kind of substance called "pheromone". Now, ACA has developed quickly and its application fields extend much wider from traveling salesman problem (TSP) to continuous space optimization, network routing, data mining and premises distribution of large scale integrated circuit etc, and good effects of these applications are gained.This paper focuses on the principles, theory and application of ACA, especially, an in-deep and systemic study on how to improve the basic ACA algorithm, solving the problems of combinatorial optimization, function optimization and quality of service (QoS) of network routing. The main works and innovation as follows:This paper proposes a new ant colony optimization algorithm, based on a more reasonable diffusion model to update the local pheromone and a disturbance strategy to update the global pheromone. The algorithm shows some advantages in improving the search speed and enhancing collaboration between the ants. As an example of TSP, the simulation results show that the algorithm has better global convergence, robustness and validity.The optimization algorithm proposed above adds the characteristics of particle swarm optimization algorithm, which makes ant particles and the initial random solution ensuring a good overall, avoids the defects of easy to fall in local optima. The algorithm is successfully applied to solve a continuous function optimization problem. The experimental results show that it has been given very good results in both one-dimensional and multidimensional function optimization instances.The paper proposes the chaotic ant colony algorithm to solve the problem of QoS multicast routing. The algorithm uses the chaotic behavior adjustment of individual ants, and chaotic factor as pheromone updating strategy to improve the defect in the convergence of ACA for solving QoS multicast routing problem. Simulation results show that the algorithm is effective.