Parallel metaheuristics: recent advances and new trends

By Enrique Alba, Gabriel Luque, Sergio Nesmachnow. International Transactions in Operational Research, 2012

300 citations recorded by the source corpus.

The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/512081f1505c7583c468b491b873c54b69e92681

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Citations in the corpus

A comprehensive survey on recent metaheuristics for feature selection (2022, background): “present the advantages and opportunities that can be realized using parallel metaheuristics in their comprehensive studies [276, 277].”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, methodology): “Some metaheuristic algorithms like population-based are slow solvers (Alba et al., 2013).”
Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy (2017, methodology): “An island-model approach [61] can be used, so that the reference set is divided into subsets ( islands ) where the eSS is executed isolated and sparse individual exchanges are performed among islands to link different subsets.”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “These methods can be called multi-population, multi-meme, or island-based methods, depending on their nature [71, 72].”