Ant Colony Optimization for Traveling Salesman Problem with Modified Pheromone Update Formula

Abstract

Traveling Salesman Problem is a combinatorial problem from which various other problems have been derived in the real-world application. It is a well-known NP-complete problem. Its instances are used in various fields around the globe. There have been various optimization techniques that are used to solve this problem. The Ant Colony Optimization (ACO) is an optimization method that is very useful in solving various artificial intelligence problems and obtaining the optimized solution. There have been methods proposed after its introduction in 1991. When using the traditional ACO pheromone update formula on the large dataset of Traveling Salesman Problem, one might get an optimal solution at the cost of a great amount of time. In this paper, we have proposed a modification in the basic Ant Colony Optimization pheromone update formula for discovering the optimized solution for the Traveling Salesman Problem using the probability from the pheromone value from succeeding nodes. This updated formula also helps in reducing the time to obtain the optimal solution as compared to the traditional formula.