Ant Colony Optimization Algorithms And Their Applications

Ant Colony Optimization (ACO)is an algorithmic approach, inspired by the foraging behavior of the real ants, which can be applied to many problems. In this paper, two kinds of ACO algorithms have been proposed for solving the traveling salesman problems and continuous space optimization. The main works and innovations as follows:1. Ant Colony Optimization algorithm(ACO)has the limitations of poor convergence, and is easy to fall in local optima. This paper propose a novel ant colony algorithm based on the improved pheromone diffusion model, this algorithm formulate the new rule of pheromone diffusion, and a disturbance mechanism is added to ant colony optimization for improving the new algorithms'performance. The simulation of TSP problem show that this algorithm has a satisfied global convergence.2. A hybrid optimization algorithm (ACOAL), in which Alopex algorithm is embedded into the improved ant colony optimization algorithm, is proposed for searching continuous space optimization. In the algorithm, the new pheromone updating rule and the moving strategy of ants are defined. The algorithm is with the rapid search capability of the improved Alopex algorithm and the good search characteristics of the improved ant colony optimization algorithm. Simulation results show that the algorithm is effective.3. By adding gradient information to influence the update of the local search of the ants, a kind of ant colony optimization algorithm with gradient information is proposed. In the new algorithms, some ants search the good result by stochastically searching in local search processing, some ants search the good result by gradient information. Simulation results show that the algorithm is effective.