Tabu Search
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The Complexity of the Approximation of the Bandwidth Problem
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Reducing the bandwidth of sparse symmetric matrices
ACM '69 Proceedings of the 1969 24th national conference
A genetic programming approach to the matrix bandwidth-minimization problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
PC2PSO: personalized e-course composition based on Particle Swarm Optimization
Applied Intelligence
An improved heuristic for the bandwidth minimization based on genetic programming
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Adaptive cooperative particle swarm optimizer
Applied Intelligence
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In this paper, the problem of reducing the bandwidth of sparse matrices by permuting rows and columns is addressed and solved with a new hybrid heuristic which combines the Particle Swarm Optimization method with Hill Climbing (PSO-HC). This hybrid approach exploits a compact PSO in order to generate high-quality renumbering which is then refined by means of an efficient implementation of the HC local search heuristic. Computational experiments are carried out to compare the performance of PSO-HC with the well-known GPS algorithm, as well as some recently proposed methods, including WBRA, Tabu Search and GRASP_PR. PSO-HC proves to be extremely stable and reliable in finding good solutions to the bandwidth minimization problem, outperforming the currently known best algorithms in terms of solution quality, in reasonable computational time.