Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows
Beam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive bounding information for choosing between different partial solutions during the solution construction process. In this work we present the use of stochastic sampling as a useful alternative to bounding information in cases were computing accurate bounding information is too expensive. As a case study we choose the well-known travelling salesman problem with time windows. Our results clearly demonstrate that Beam-ACO, even when bounding information is replaced by stochastic sampling, may have important advantages over standard ACO algorithms.
KeywordsTravel Salesman Problem Partial Solution Constraint Violation Assembly Line Balance Heuristic Information
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- 5.Juillé, H., Pollack, J.B.: A sampling-based heuristic for tree search applied to grammar induction. In: Proceedings of AAAI 1998 – Fifteenth National Conference on Artificial Intelligence, pp. 776–783. MIT Press, Cambridge (1998)Google Scholar
- 6.Ruml, W.: Incomplete tree search using adaptive probing. In: Proceedings of IJCAI 2001 – Seventeenth International Joint Conference on Artificial Intelligence, pp. 235–241. IEEE Press, Los Alamitos (2001)Google Scholar