Black Hole Algorithm and Its Applications

Kumar, Santosh; Datta, Deepanwita; Singh, Sanjay Kumar

doi:10.1007/978-3-319-11017-2_7

Cited by 68 publications

(38 citation statements)

References 71 publications

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“…Moreover, this method uses a technique of exploration/exploitation free of external components reducing the probability of being affected to unexpected changes. Finally, as reported in [ 68 ], the black hole algorithm in optimization problem converges to global optimal in each evaluation while its competitors' genetic algorithm, ant colony optimization, and simulated annealing can be caught in local optimum solutions.…”

Section: Methodsmentioning

confidence: 99%

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Castro-Muñoz

Olivares

Taramasco

et al. 2018

Computational Intelligence and Neuroscience

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Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.

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Section: Methodsmentioning

confidence: 99%

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Castro-Muñoz

Olivares

Taramasco

et al. 2018

Computational Intelligence and Neuroscience

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“…Central force optimization in 2007 5 Method develop from the theory of gravitational kinematics Gravitational local search (GLSA) in 2013 6 The main features of this heuristics derived from ''Newton's law of gravitation,'' namely a gravitational search algorithm Black hole algorithm in 2015 27 The theme of black hole is used to develop the bio-inspired algorithm Charged system search in 2010 28 An approach taken from the behavior of charges and it is based on the Coulomb law and motion laws Ray optimization algorithm in 2014 29 The law for light ''Snell's light refraction law'' is mapped into algorithm for solving different problems Artificial chemical reaction optimization algorithm published in 2011…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Intelligent clustering using moth flame optimizer for vehicular ad hoc networks

Ishtiaq

Ahmed

Khan

et al. 2019

International Journal of Distributed Sensor Networks

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Vehicular ad hoc networks consist of access points for communication, transmission, and collecting information of nodes and environment for managing traffic loads. Clustering can be performed in the vehicular ad hoc networks for achieving the desired goals. Due to the random range of vehicular ad hoc networks, stability is the major issue on which major research is still in progress. In this article, a moth flame optimization-driven clustering algorithm is presented for vehicular ad hoc networks, replicating the social behavior of moth flames in creating efficient clusters. The proposed framework is extracted from the living routine of moth flames. The proposed framework allows efficient communication by creating the augmented number of clusters due to which it is termed as intelligent algorithm. Besides this, the use of unsupervised clustering technique emphasizes to call it as an intelligent clustering algorithm. The recommended intelligent clustering using moth flame optimization framework is executed for resolving and optimizing the clustering problem in vehicular ad hoc networks, the primary focus of the proposed scheme is to improve the stability in vehicular ad hoc networks. This proposed method can also be used for the transmission of data in vehicular networks. Intelligent clustering using moth flame optimization is then proved by relative study with two variants of particle swarm optimization: multiple-objective particle swarm optimization and comprehensive learning particle swarm optimization and a variant of ant colony optimization: ant colony optimization-based clustering algorithm for vehicular ad hoc network. The simulation demonstrates that the intelligent clustering using moth flame optimization is provisioning optimal outcomes in contrast to widely known metaheuristics. Furthermore, it provides a robust routing mechanism based on the clustering. It is suitable for large highways for the productivity of quality communication, reliable delivery for each vehicle and can be widely applicant.

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“…The idea of black holes was established in the 18th century by John Michel and Pierre Simon de Laplace, and the first black hole was recognized by John Wheeler in 1967 [61,62]. A common type of black hole is created by dying stars with very large masses, when the gravity force won against pressure.…”

Section: Black Hole Algorithm-based Optimizationmentioning

confidence: 99%

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Bányai

2018

Energies

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Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

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Black Hole Algorithm and Its Applications

Cited by 68 publications

References 71 publications

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Intelligent clustering using moth flame optimizer for vehicular ad hoc networks

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

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