In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs. Artificial ants represent multi …

2021年6月24日 · In this paper, we develop and implement a Bayesian simulation model derived from the active inference framework that captures some of the underlying dynamics and emergent phenomena of ant colony foraging behavior. In our model, we focus on the stigmergic outcomes of foragers using a single trail pheromone molecule. Assuming that each agent ...

Many special cases of the ACO metaheuristic have been proposed. The three most successful ones are: Ant System, Ant Colony System (ACS), and MAX-MIN Ant System (MMAS). For illustration, example problem used is Travelling Salesman Problem.

2020年5月17日 · Ant Colony Optimization technique is purely inspired from the foraging behaviour of ant colonies, first introduced by Marco Dorigo in the 1990s. Ants are eusocial insects that prefer community survival and sustaining rather than as individual species.

2005年12月1日 · Ant colony optimization is a technique for optimization that was introduced in the early 1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies.

2014年5月11日 · In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging.

Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solv-ing optimization problems. From the early nineties ...

2024年7月1日 · This study proposed a historical-information-based patch-level multi-type ant colony optimization (HI-PMACO) model to examine the effect of incorporating historical information into the optimization model from both macro and local perspectives.

2024年2月1日 · We propose an Ant-Colony Optimization algorithm for the search of test models satisfying the objectives of maximizing internal diversity and maximizing external diversity. We provide a tool prototype that implements this approach and generates the models in the well-established XMI interchange format.

2023年9月22日 · In this subsection, we introduce the hierarchical temporal memory model into the ant colony algorithm to retain previous path information. By conducting layered path optimization during the ants’ exploration process, we aim to improve the quality of solutions and help the ant colony escape local optima. The model diagram is shown in Fig. 4.