Multi-Colony Collaborative Ant Optimization Algorithm Based on Cooperative Game Mechanism

Abstract

Aiming at the problem that ant colony algorithm is easy to fall into local optima and slow convergence, a multi-colony collaborative ant optimization algorithm based on cooperative game mechanism(CCACO) is proposed. First of all, this article presents pheromone matrix adaptive matching strategy and improved generative adversarial nets (GAN) model according to cooperative game mechanism. The pheromone matrix adaptive matching strategy allocates pheromone matrix for each colony to maximize the overall benefit of the colonies by establishing a cooperative game model. The improved GAN model is built according to the game between the global optimal solution and the new solution of each colony, which improves the convergence speed of the algorithm and the quality of solutions. In addition, collaborative optimization mechanism is proposed to improve the quality of the solution. In this mechanism, cloning strategy binds the cities on each common path together, which increases the pheromone concentration of the common path to accelerate the algorithm convergence; central diffusion strategy diffuses pheromones from central cities to nearby cities, which increases the diversity of solutions; forward and backward propagators adjust the amount of pheromone release to regulate the convergence rate of the algorithm. Finally, information entropy is used to measure the diversity of CCACO. When the value of information entropy is less than threshold value, cooperative game mechanism and collaborative optimization mechanism to regulate the relationship between the convergence speed and the quality of the solution. Experimental results in the TSPLIB standard library demonstrate that the proposed algorithm outperforms other state-of-the-art multi-colony ant colony optimization algorithms. The algorithm is applied to robot path planning, which reflects the practicality of the algorithm.