The ant colony optimization meta-heuristic
New ideas in optimization
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Future Generation Computer Systems
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Computational Intelligence in Telecommunications Networks
Computational Intelligence in Telecommunications Networks
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Ant Colony Optimization
Network design with node connectivity constraints
LANC '03 Proceedings of the 2003 IFIP/ACM Latin America conference on Towards a Latin American agenda for network research
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
Cellular Genetic Algorithms
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey on parallel ant colony optimization
Applied Soft Computing
Parallel multi-objective Ant Programming for classification using GPUs
Journal of Parallel and Distributed Computing
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The development of exact and heuristic algorithms for communication network design requires ever-growing amounts of computational power. In particular, finding a dependable, fault-tolerant network topology can be modelled as the generalised Steiner problem (GSP). This problem belongs to the NP-hard class, so that exact methods cannot be applied to real life sized problems. An alternative is using metaheuristics, but even in this case the computation time can quickly grow leading to extremely long runs or to degraded quality results. In this paper, we discuss the use of parallel implementations as a means to tackle this computational performance bottleneck. In particular, we concentrate on the ant colony optimisation (ACO) metaheuristic. We review previous ACO approaches for solving the GSP, as well as literature on parallelisation of this method. We propose and develop a new parallel model suitable for ACO, called cellular ACO, which is then applied to the GSP. We present computational results for large GSP instances, showing that cellular ACO finds high quality solutions, comparable to the best published sequential and parallel metaheuristics, while attaining a large speedup, resulting in very good computational efficiency.