Abstract
The Ant Colony Optimization (ACO) is a probabilistic technique inspired by the behavior of ants for solving computational problems that may be reduced to finding the best path through a graph. Some species of ants deposit pheromone on the ground to mark some favorable paths that should be used by other members of the colony. Ant colony optimization implements a similar mechanism for solving optimization problems. In this paper a warm-up procedure for the ACO is proposed. During the warm-up, the pheromone matrix is initialized to provide an efficient new starting point for the algorithm, so that it can obtain the same (or better) results with fewer iterations. The warm-up is based exclusively on the graph, which, in most applications, is given and does not need to be recalculated every time before executing the algorithm. In this way, it can be made only once, and it speeds up the algorithm every time it is used from then on. The proposed solution is validated on a set of traveling salesman problem instances, and in the simulation of a real industrial application for the routing of pickers in a manual warehouse. During the validation, it is compared with other ACO adopting a pheromone initialization technique, and the results show that, in most cases, the adoption of the proposed warm-up allows the ACO to obtain the same or better results with fewer iterations.
Highlights
The proposed solution is validated on a set of traveling salesman problem instances, and in the simulation of a real industrial application for the routing of pickers in a manual warehouse
The warm-up procedure proposed in this paper aims to carry out a fine-tuning of the pheromone matrix on a specific graph, so that, every time the Ant Colony Optimization (ACO) is executed, it starts from an already weighted graph, where the promising paths were highlighted with a high level of pheromone and the bad paths excluded a priori
The results obtained by the proposed ACO with warm-up (ACOWU) are written in bold when it outperformed the classic
Summary
The proposed solution is validated on a set of traveling salesman problem instances, and in the simulation of a real industrial application for the routing of pickers in a manual warehouse During the validation, it is compared with other ACO adopting a pheromone initialization technique, and the results show that, in most cases, the adoption of the proposed warm-up allows the ACO to obtain the same or better results with fewer iterations. The ACO, like all the evolutionary algorithms, needs many iterations to converge to a good solution, and, in the case of large-size problems, this process can be very time-consuming [3] For this reason, the implementation of the ACO for solving large-size problems in real-time (i.e., a few seconds or even less) might be problematic. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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