Adaptive Dynamic Probabilistic Elitist Ant Colony Optimization in Traveling Salesman Problem

• Published in: SN computer science Vol. 1; no. 2; p. 95
• Main Authors: Chatterjee, Amrita, Kim, Eunjin, Reza, Hassan
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2020; Springer Nature B.V
• Subjects: Advances in Computational Intelligence; Algorithms; Ant colony optimization; Cities; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Cooperation; Data Structures and Information Theory; Elitism; Foraging behavior; Graph representations; Heuristic methods; Information Systems and Communication Service; Optimization algorithms; Original Research; Paradigms and Applications; Pattern Recognition and Graphics; Pheromones; Random variables; Software Engineering/Programming and Operating Systems; Statistical analysis; Transition rules; Travel; Traveling salesman problem; Vision; Elitist ACO; Original ACO; Ant system (AS); Ant colony optimization (ACO); Probabilistic elitist ACO
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-0083-z

The ant colony optimization (ACO) is a population-based metaheuristic algorithm for the optimization problem, inspired by the foraging behavior of ants in the ant colony. One of its variants, the elitist ACO, further reinforces itself with the additional pheromone deposit to find the best path. Even though this usually leads to converging on the solution faster, it also has the drawback of getting stuck in local minima. In this paper, we describe a variation on elitist ACO where the pheromone contribution of the best path is further predicated by a probability factor. This probabilistic elitist ACO often produces a better solution for TSP, at the cost of a higher number of iterations. Some experimental results of this probabilistic elitist ACO are presented.