Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

• URL: http://arxiv.org/abs/2010.11523v2
• Date: Fri, 13 Nov 2020 08:54:29 GMT
• Title: Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems
• Authors: Jorik Jooken, Pieter Leyman, Patrick De Causmaecker, Tony Wauters
• Abstract summary: This approach makes use of a algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees.
• Score: 0.6882042556551609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. They are demonstrated for two specific combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. Computational results show that the algorithm achieves promising results for both problems and eight new best solutions for a benchmark set of instances are found for the former problem. These results indicate that the algorithm is competitive with the state-of-the-art. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics.

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