Parallel Ant Colony Optimization on Graphics Processing Units

TL;DR: A basic ACO algorithm named the ant system (AS) to solve the TSP, which involves using many artificial ants to perform parallel searches on a graph to find a minimum-weight Hamilton cycle in a complete weighted directed graph.

TL;DR: The quantity performances achieved on the same dataset are compared between the heuristic and data-driven methods to show the strengthening of the data- driven methods.

TL;DR: A review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems, and insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.

TL;DR: A better variation of CLPSO is proposed, called the parallel comprehensive learning particle swarm optimizer (PCLPSO) which has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently.

TL;DR: Two algorithms for solving the (symmetric distance) traveling salesman problem have been programmed for a high-speed digital computer and are based on a general heuristic approach believed to be of general applicability to various optimization problems.

TL;DR: An artificial ant colony capable of solving the travelling salesman problem (TSP) is described, an example of the successful use of a natural metaphor to design an optimization algorithm.

TL;DR: In this article, a new ACO model that overcomes the difficulties found when working with a huge construction graph is presented. But it is not suitable when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it.

TL;DR: The empirical tests are performed applying MAX-MIN Ant System, one of the most efficient ACO algorithms, to the Traveling Salesman Problem and show that using parallel independent runs is very effective.