A Binary Firefly Algorithm for the Set Covering Problem

Crawford, Broderick; Soto, Ricardo; Olivares-Suárez, Miguel; Paredes, Fernando

doi:10.1007/978-3-319-06740-7_6

Cited by 47 publications

(38 citation statements)

References 26 publications

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“…The transfer function is the beginning of the binarization method that allows to map the ℝ n solutions in [0,1] n solutions. The S shaped and the V shaped are the most used transfer functions, well described in [37,38]. The next step is applying a rule to binarize the transfer function results, which could include the binarization rules elitist, the static probability, the complement, or the roulette [37].…”

Section: Binarization Methodsmentioning

confidence: 99%

“…For the reliability analysis of the bulk power system, a transfer function based on swarm intelligence was used [40]. A binary coded firefly algorithm that solves the set covering problem was performed using the same transfer function [37]. A binary cuckoo search algorithm for solving the set covering problem was applied previously [41].…”

Section: Binarization Methodsmentioning

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: Binarization Methodsmentioning

confidence: 99%

Section: Binarization Methodsmentioning

confidence: 99%

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

et al. 2018

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“…The earlier applications showed that BA could solve different optimization problems and proved that its efficiency and robustness compared to different algorithms such as GA and PSO [20][21][22]. A new trend in bat algorithms is focusing on hybridizing BA with different strategies [23][24][25][26][27][28][29][30][31]. Fister et al [23] developed a hybrid BA based on various evolution strategies for solving optimization tasks, while Baziar et al [24] proposed a modified BA based on adaptive self-strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Fister et al [28] introduced a new version of BA based on self-adaptation of control parameters. Further, binary versions of BA were developed in [29][30][31]. Mirjalili et al [29] introduced a binary version of BA by employing a V-shaped transfer function to overcome the drawback of the sigmoid transfer function which keeps the positions unchanged during the iterations of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In [30], authors developed an integrating version of the binary BA based on Naïve Bayes classifier for feature selection problem. In [31], a binary vision of BA is established based on the sigmoid transfer function for solving different optimization problems. Due to continuous nature of BA, it is still in its infancy for solving combinatorial optimization problems, so this is also the motivation behind this study.…”

Section: Introductionmentioning

confidence: 99%

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New binary bat algorithm for solving 0–1 knapsack problem

Rizk‐Allah

Hassanien

2017

Complex Intell. Syst.

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This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA-LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA-LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA-LSS can be promise alternative for solving large-scale 0-1 knapsack problems.

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