document: In this paper, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperation protocol based on reinforcement learning and pattern matching. Good patterns that make up improving solutions are identified and shared by the agents. This agent-based system aims to provide a modular flexible framework to deal with a variety of different problem domains. We have evaluated the performance of this approach using the proposed framework which embodies a set of well known metaheuristics with different configurations as agents on two problem domains, Permutation Flow-shop Scheduling and Capacitated Vehicle Routing. The results show the success of the approach yielding three new best known results of the Capacitated Vehicle Routing benchmarks tested, whilst the results for Permutation Flow-shop Scheduling are commensurate with the best known values for all the benchmarks tested.
doc_bio_tags: O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O B I I I O O B I O B I B I O O O O O O O O O O O B O O O O O O O O O B O B I I I I I I I O O O O O O O B O O O B O B I I I I I I I I I O B O B I I O O O O O O O O O O O O O O B O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: adapts a direct cooperation protocol agents approach capacitated vehicle routing benchmarks deal with a variety of different problem domains embodies a set of well known metaheuristics with different configurations framework good patterns pattern matching reinforcement learning two problem domains
abstractive_keyphrases: agent-based system each agent is implementing a different metaheuristic/local search combination permutation flow-shop scheduling permutation flow-shop scheduling and capacitated vehicle routing provide a modular flexible framework this approach