Firefly algorithm and ant colony algorithm to optimize the traveling salesman problem

Abstract

Through the study of ACOTSP, it is found that the previous ant colony algorithm will fall into local optimal when increasing pheromone concentration factor. In order to solve the problem, we through the improved pheromone concentration factor to view your traveling salesman solving process, through the simulation experiments found that due to the pheromone concentration gradually increased with the number of iterations, pheromone concentration and pheromone concentration factor exponential relationship, lead to appear even if the distance is large move also can appear probability is very high. In this design, the ant colony algorithm is optimized by introducing firefly algorithm (FA): the ant colony movement deviation is avoided by adding disturbance factor; and the migration probability caused by excessive pheromone concentration is solved by adding function relation between moving distance and pheromone concentration. Simulation results show that the optimized algorithm has better results and is not easy to fall into local optimum.

An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

“…The path nodes constructed by the ant colon algorithm, as shown in Figure 2a, exhibit intersections, resulting in a path length o 7639. 31. By implementing local optimization, the cross-interference can be eliminated.…”

“…Shahadat et al [30] adopted the general formula of visibility heuristic associated with the final destination city to intelligently deal with the problem of returning to the starting city. Yu et al [31] found that the traditional ant colony algorithm would fall into the local optimum when increasing the pheromone concentration factor. To solve this problem, a disturbance factor was added to the algorithm to eliminate the interference of other factors on the ant colony movement, and then the functional relationship between the moving distance and the pheromone concentration was increased.…”

Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network

“…The research is based on static networks and considers the dynamic uncertainty of the scene less. In [ 12 ], the routing algorithm based on the Firefly algorithm to optimize FCM (Firefly algorithm to optimize fuzzy C-means, FFACM) establishes a cost function by calculating the link cost between nodes, which improves the energy consumption of nodes. In addition, the routing overhead can also be reduced by probabilistic broadcasting based on trust [ 13 ].…”