Abstract
The chapter presents a general picture of parallel meta-heuristic search for optimization. It recalls the main concepts and strategies in designing parallel meta-heuristics, pointing to a number of contributions that instantiated them for neighborhood- and population-based meta-heuristics, and identifies trends and promising research directions. The focus is on cooperation-based strategies, which display remarkable performances, in particular strategies based on asynchronous exchanges and the creation of new information out of exchanged data to enhance the global guidance of the search.
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Acknowledgements
The author wishes to acknowledge the contributions of colleagues and students, in particular Professors Michel Gendreau, Université de Montréal, Canada, and Michel Toulouse, the Vietnamese-German University, Vietnam, who collaborated over the years to the work on parallel meta-heuristics for combinatorial optimization. All errors are solely and entirely due to the author, however.
Partial funding for this project has been provided by the Natural Sciences and Engineering Council of Canada (NSERC), through its Discovery Grant and the Discovery Accelerator Supplements programs, and the Strategic Clusters program of the Fonds québécois de la recherche sur la nature et les technologies. The author thanks the two institutions for supporting this research.
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Crainic, T.G. (2018). Parallel Metaheuristic Search. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_40
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