A Multi Dynamic Binary Black Hole Algorithm Applied to Set Covering Problem

García, José; Crawford, Broderick; Soto, Ricardo; García, Pablo M.

doi:10.1007/978-981-10-3728-3_6

Cited by 43 publications

(14 citation statements)

References 19 publications

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“…There are different methods for developing the binary version of a continuous heuristic algorithm while preserving the principles inspiring the search process. Examples of such binarizations are harmony search (HS) [26], the differential evolution algorithm (DE) [27][28][29][30], particle swarm optimization (PSO) [31], the magnetic optimization algorithm (MOA) [32], the gravitational search algorithm (GSA) [33], the firefly algorithm (FA) [34], the shuffled frog leaping algorithm (FLA) [35], the fruit fly optimization algorithm (FFA) [36], the cuckoo search algorithm (CSA) [37], the cat swarm optimization algorithm (CSOA) [38], the bat algorithm [39], the Black Hole Algorithm [40], the algae algorithm (AA) [41], and fireworks [42].…”

Section: Complexitymentioning

confidence: 99%

Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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Section: Complexitymentioning

confidence: 99%

Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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“…Metaheuristics have also been applied to solve SCP, some examples are genetic algorithms [30], simulated annealing [31], and ant colony optimization [32]. More recently, swarm-based metaheuristics as cat swarm [33], artificial bee colony [34], and black hole [9] were also proposed.…”

Section: Crew Scheduling Problemsmentioning

confidence: 99%

“…Spark has a high performance in parallel computing, being used in machine learning algorithms [65], imaging processing [66], bioinformatics [67], computational intelligence [9], astronomy [68], medical information [69], and so on.…”

Section: Spark Distributed Frameworkmentioning

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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“…This has been used in machine learning algorithms [47], imaging processing [48], bioinformatics [49], computational intelligence [50], astronomy [51], medical information [52], and so on.…”

Section: Spark Distributed Frameworkmentioning

confidence: 99%

A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

2017

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Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

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A Multi Dynamic Binary Black Hole Algorithm Applied to Set Covering Problem

Cited by 43 publications

References 19 publications

Putting Continuous Metaheuristics to Work in Binary Search Spaces

Putting Continuous Metaheuristics to Work in Binary Search Spaces

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

A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

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