Parallel Metaheuristics: A New Class of Algorithms

By Enrique Alba. 2005

631 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1c8f03290e2961fde865e3069055f9ddc3ca038e

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In Industrial and Manufacturing Engineering

Citations in the corpus

A survey on new generation metaheuristic algorithms (2019, background): “Alba (2005) gives a comprehensive survey on parallel GA (Alba and Troya, 1999). The author defines parallel GAs as a new kind of metaheuristics. In addition to parallel GA, the parallel versions of GP, SS, ACO, SA, VNS, TS, hybrid, heterogeneous, and multiobjective are evaluated. The theoretical fou”
Parallel metaheuristics: recent advances and new trends (2012, methodology): “Alba et al. / Intl. Trans. in Op. Res. 20 (2013) 1–48”
Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art (2021, background): “…it could be an interesting future research direction to explore the parallelism concept in the integration of ML techniques into MHs using GPU (Graphics Processing Units) and TPUs (Tensor Processing Units) accelerators ( Alba, 2005; Cahon, Melab, & Talbi, 2004; Van Lu-ong, Melab, & Talbi, 2011 ).”
A survey on optimization metaheuristics (2013, background): “Parallel EAs have also deserved interest in the recent past (a good review can be found in [5]).”
Bio-inspired computation: Where we stand and what's next (2019, background): “In this context distributed Evolutionary Algorithms (dEA) come to the scene by deploying different populations of solutions on distributed systems in order to improve the performance of sequential (traditional) evolutionary solvers [207, 208].”
Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, background): “Parallel metaheuristics (Toulouse et al. 1996, Alba 2005, Crainic and Toulouse 2010) are concerned with the efficient exploitation of simultaneous work (often on several processors ) to solve a given problem instance, and have proved of great interest for routing problems (Crainic 2008).”
Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy (2017, background): “The parallelization of metaheuristics pursues one or more of the following goals: to increase the size of the problems that can be solved, to speed-up the computations, or to attempt a more thorough exploration of the solution space [59, 60].”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, methodology): “Mainly three parallelism techniques have been recorded in literature as they are (a) parallel moves model, (b) parallel multi-start model, and (c) move acceleration model (Alba et al. 2005).”
Metaheuristic research: a comprehensive survey (2018, background): “According to the survey, authors (Alba 2005) revealed the usefulness of quantum computing into evolutionary algorithms.”
Hybrid metaheuristics in combinatorial optimization: A survey (2011, methodology): “For an overview on parallel hybrid metaheurisics we recommend [26,27].”
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].”