A Pseudo-dynamic Search Ant Colony Optimization Algorithm with Improved Negative Feedback Mechanism to Solve TSP

Abstract

Aiming at the problem that the ant colony optimization algorithm with improved negative feedback mechanism is easy to fall into the local optimum when solving the traveling salesman problem, a pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism is proposed. A pseudo-dynamic search state transition rule is introduced to make the ant search not limited to pheromone concentration and distance between cities, which enhance the ability to jump out of local optimum. At the same time, the weight of pheromone concentrations in the optimal paths are updated to increase the convergence speed. The simulation results of different data sets in TSPLIB standard library show that the improved algorithm not only has higher convergence accuracy, but also outperforms the local optimum obviously better than the negative feedback ant colony algorithm.