Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Design of Stacked Self-Healing Rings Using a Genetic Algorithm
Journal of Heuristics
Solving the Two-Connected Network with Bounded Meshes Problem
Operations Research
Advanced Modelling Techniques for Designing Survivable Telecommunications Networks
BT Technology Journal
Two-Connected Networks with Rings of Bounded Cardinality
Computational Optimization and Applications
Optimal Design of Reliable Computer Networks: A Comparison of Metaheuristics
Journal of Heuristics
An Evolutionary Design Algorithm for Ring-based SDH optical core networks
BT Technology Journal
Grasp Embedded Scatter Search for the Multicommodity Capacitated Network Design Problem
Journal of Heuristics
Graphs, Networks and Algorithms (Algorithms and Computation in Mathematics)
Graphs, Networks and Algorithms (Algorithms and Computation in Mathematics)
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The particle swarm optimisation (PSO) is a stochastic population-based global optimisation technique modelled on the social behaviour of bird flocks or fish schooling. This paper investigates the use of PSO for designing minimum cost two-connected networks such that the shortest cycle to which each edge belongs to does not exceed a given length. PSO is a relatively new metaheuristic in which particles were originally designed to handle a continuous solution space. Given that the topological network design problem is a highly constrained discrete combinatorial optimisation, we modify the particle position representation and the particle velocity update rule by introducing an oscillating mechanism to better adapt a standard PSO for the problem. We provide numerical results based on randomly generated graphs found in the literature and compare the solution quality with that of tabu search and genetic algorithms. An empirical study for network sizes up to 30 nodes and a comparison with tabu search and genetic algorithms shows the potential of using PSO for the problem. To the best of our knowledge, this is the first attempt to implement particle swarm optimisation for the aforementioned problem.