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 parall...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Optimization methods & software Ročník 34; číslo 6; s. 1231 - 1250
Hlavní autoři: Souravlias, D., Kotsireas, I. S., Pardalos, P. M., Parsopoulos, K. E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 02.11.2019
Taylor & Francis Ltd
Témata:
Algorithm portfolios
Algorithms
Combinatorial analysis
combinatorial optimization
Forecasting
Forecasting techniques
Mathematical models
metaheuristics
parallel algorithms
performance forecasting
Resource allocation
Statistical analysis
Tabu search
ISSN:1055-6788, 1029-4937
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1055-6788
1029-4937
DOI:10.1080/10556788.2018.1484123