A biased random-key genetic algorithm for the capacitated minimum spanning tree problem
Tipo de documentoArtículo
Fecha de publicación2015-05
Condiciones de accesoAcceso restringido por política de la editorial (embargado hasta 2018-06-01)
This paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost.The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectivenes sof the approach:Seven newbest- known solutions are presented for the set of benchmark instances used in the experiments.
CitaciónRuiz, H. [et al.]. A biased random-key genetic algorithm for the capacitated minimum spanning tree problem. "Computers & operations research", Maig 2015, vol. 57, p. 95-108.
Versión del editorhttp://www.sciencedirect.com/science/article/pii/S0305054814003153