Ant Colony Optimization Algorithm Based on Adaptive Weight and Volatility Parameters

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as traveling salesman problems (TSP). However, it is necessary to set the parameters of ACO algorithm in manual way, in practice. This paper proposes a novel ACO algorithm with several automatic settings including adaptive weight parameter and adaptive volatility rate of pheromone trail. The adaptive weight parameter is used to control the relative weight of pheromone trail and heuristic value. And the adaptive volatility rate of pheromone trail runs according to the quality of the solutions found by artificial ants. Then, the proposed algorithm can be proved to converge to the global optimal solution. Finally, the experimental results of computing traveling salesman problems and film-copy deliverer problems also indicate that the proposed ACO approach is more effective than other ant methods and non-ant methods.