Parallel algorithm portfolios with performance forecasting
We propose a novel algorithm portfolio model that incorporates time series forecasting techniques to predict online the performance of its constituent algorithms. The predictions are used to allocate computational resources to the algorithms, accordingly. The proposed model is demonstrated on parallel algorithm portfolios consisting of three popular metaheuristics, namely tabu search, variable neighbourhood search, and multistart local search. Moving average and exponential smoothing techniques are employed for forecasting purposes. A challenging combinatorial problem, namely the detection of circulant weighing matrices, is selected as the testbed for the analysis of the proposed approach. Experimental evidence and statistical analysis provide insight on the performance of the proposed algorithms and reveal the benefits of using forecasting techniques for resource allocation in algorithm portfolios.