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A generic agent-based framework for cooperative search using pattern matching and reinforcement learning

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Cooperative search provides a class of strategies to design more effective search methodologies through combining (meta-) heuristics for solving combinatorial optimisation problems. This area has been little explored in operational research. In this study, we propose a general agent-based distributed framework where each agent implements a (meta-) heuristic. An agent continuously adapts itself during the search process using a cooperation protocol based on reinforcement learning and pattern matching. Good patterns which make up improving solutions are identified and shared by the agents. This agentbased system aims to raise the level of generality by providing a flexible framework to deal with a variety of different problem domains. The proposed framework has been so far tested on Permutation Flow-shop Scheduling and Travelling Salesman Problem instances yielding promising results.
Original languageEnglish
JournalEuropean Journal of Operational Research
Publication statusPublished - 4 Dec 2011

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