Abstract
In this paper we propose a new tabu search hyperheuristic which makes individual low level heuristics tabu dynamically using an analogy with the Binary Exponential Back Off (BEBO) method used in network communication. We compare this method to a reduced Variable Neighbourhood Search (rVNS), greedy and random hyperheuristic approaches and other tabu search based heuristics for a complex real world workforce scheduling problem. Parallelisation is used to perform nearly 155 CPU-days of experiments. The results show that the new methods can produce results fitter than rVNS methods and within 99% of the fitness of those produced by a highly CPU-intensive greedy hyperheuristic in a fraction of the time.
This work was funded by EPSRC and Trimble under an EPSRC CASE studentship, made available through the Smith Institute for Industrial Mathematics and System Engineering.
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Remde, S., Dahal, K., Cowling, P., Colledge, N. (2009). Binary Exponential Back Off for Tabu Tenure in Hyperheuristics. In: Cotta, C., Cowling, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2009. Lecture Notes in Computer Science, vol 5482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01009-5_10
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