Ant Colony Optimization Algorithm Based on Dynamic Recombination and Co- operative Communication Strategy

Abstract

Aiming at the problems of traditional ant colony algorithm in solving traveling salesman problem (TSP), such as slow convergence speed and easy to fall into local optimum, an ant colony optimization algorithm (RCACO) based on dynamic recombination and cooperative communication strategy is proposed. Firstly, the ant colony is divided into greedy ant colony and exploratory ant colony. The two ant colonies implement different path construction rules and pheromone update strategies to balance the convergence speed and diversity of the algorithm. Secondly, a new dynamic recombination operator based on the clew binary tree is adopted, and the solution set is dynamically recombined according to different strategies to improve the diversity of the algorithm. Furthermore, a collaborative communication strategy based on similarity and potential value is proposed. From a global perspective, the path with the most potential to become the optimal solution is found, and pheromone rewards are given to these paths to improve the convergence speed of the algorithm. Finally, the algorithm also adds a stagnation avoidance strategy to help the ant colony jump out of the local optimum and improve the accuracy of the algorithm. Through the Matlab simulation experiment of multiple groups of cases in TSPLIB, compared with the traditional ant colony algorithm and other optimization algorithms, the simulation results show that the improved ant colony algorithm significantly improves the convergence speed and solution accuracy.

Keywords

• traveling salesman problem (tsp)
• ant colony optimization (aco)
• dynamic recombination
• collaborative communication
• stagnation avoidance