الگوریتم های کلونی مورچگان و فریم ورک جاوا
Abstract
1- Motivation and significance
2- Software description
3- Illustrative example
4- Impact
5- Conclusion
References
Abstract
Ant Colony Optimisation (ACO) algorithms emulate the foraging behaviour of ants to solve optimisation problems. They have proven effective in both academic and industrial settings. ACO algorithms share many features among them. Isula encapsulates these commonalities and exposes them for reuse in the form of a Java library. In this paper, we use the travelling salesman problem and image segmentation to showcase the framework capabilities using three top-performing ACO algorithms implemented in Isula. This framework is an open-source project available at GitHub, where is currently the most popular ACO java repository.
Motivation and significance
Ant Colony Optimisation (ACO) algorithms, proposed by Dorigo et al. [1], solve optimisation problems by emulating the behaviour of ants in nature. When ants traverse a territory searching for food, they mark their path with pheromone. After they have located food, they make several trips from the food source to the nest, increasing the intensity of their path’s pheromone trail. In case several ants have located the same food source and are also transporting food to the nest, the pheromone trail of the ant with the shortest path is more intense. The shortest path, in the same amount of time, has more two-way trips than longer paths that require more time for their traversal. Fellow ants are sensitive to pheromone and tend to select the path with the most intense pheromone trail. This behaviour also increases the pheromone intensity of the shortest path. Over time, the whole colony converges towards the optimal solution.