Asynchronous Non-Generational Model to Parallelize Metaheuristics: A Bioinformatics Case Study

Santander-Jiménez, Sergio; Vega‐Rodríguez, Miguel A.

doi:10.1109/tpds.2016.2645764

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…The solutions are then grouped using K-means clusterization and the Δ i as magnitude of distance. We obtain the transition probability with the group assigned to each solution using (10). Subsequently, the transition of each solution is performed.…”

Section: T + 1 = X T + δ X Tmentioning

confidence: 99%

“…In the literature, we find metaheuristics that have satisfactorily solved problems of resource allocation [2,3], vehicle routing [4], scheduling problems [5], reshuffling operations at maritime container terminals problems [6], antenna positioning problems [7], covering problems [8,9], and also in bioinformatics problems such as protein structure prediction, molecular docking, and gene expression analysis [10]. However, in the big data era, the integration of metaheuristics into the decision-making process presents two fundamental difficulties: the first one is to get from computational intelligence algorithms, suitable results, and convergence times when dealing with large datasets, because much of the decisions must be close to real time.…”

Section: Introductionmentioning

confidence: 99%

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A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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Section: T + 1 = X T + δ X Tmentioning

confidence: 99%