Wyniki wyszukiwania - BazTech

1

A hybrid optimized resource allocation modelfor multi-cloud environment using batand particle swarm optimization algorithms

Selvapandian D., Santhosh R.

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

87--103

EN

In cloud computing, scheduling and resource allocation are the major factors that definethe overall quality of services. An efficient resource allocation module is required in cloudcomputing since resource allocation in a single cloud environment is a complex process.Whereas resource allocation in a multi-cloud environment further increases the complexityof allocation procedures. Earlier, resources from the multi-cloud environment were allocated based on task requirements. However, it is essential to analyze the present resourceavailability status and resource capability before allocating to the requested tasks. So, inthis research work, a hybrid optimized resource allocation model is presented using bat optimization algorithm and particle swarm optimization algorithm to allocate the resourceconsidering the resource status, distance, bandwidth, and task requirements. Proposedmodel performance is evaluated through simulation and compared with conventional optimization algorithms. For a set of 500 tasks, the proposed approach allocates resourcesin 47 s, with a minimum energy consumption of 200 kWh. Compared to conventionalapproaches, the performance of the proposed model is much better in terms of deadlinemissed tasks, resource requirement, energy consumption, and allocation time.

2

Multitemporal meteorological drought forecasting using Bat-ELM

Gholizadeh Reza, Yılmaz Hasan, Danandeh Mehr Ali

Acta Geophysica

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2022

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Vol. 70, no 2

917--927

EN

The advancement of the machine learning (ML) models has demonstrated notable progress in geosciences. They can identify the underlying process or causality of natural hazards. This article introduces the development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling. The multi-temporal standardized precipitation evapotranspiration index (SPEI-3 and SPEI-6) is computed as the meteorological drought index at two study regions (Beypazari and Nallihan), located in Ankara province, Turkey. The proposed hybrid model is obtained by integrating the Bat optimization algorithm as the parameter optimizer with an extreme learning machine (ELM) as the regressor engine. The efficiency of the intended model was evaluated against the classic artificial neural network (ANN) and standalone ELM models. The evaluation and assessment are conducted using statistical metrics and graphical diagrams. The forecasting results showed that the accuracy of the proposed model outperformed the benchmark models. In a quantitative assessment, the Bat-ELM model attained minimal root mean square error for the SPEI-3 and SPEI-6 (RMSE=0.58 and 0.43 at Beypazari station and RMSE=0.53 and 0.37 at Nallihan station) over the testing phase. This indicates the new model approximately 20 and 15% improves the forecasting accuracy of traditional ANN and classic ELM techniques, respectively.

3

Estimation of air overpressure using bat algorithm

Dehghani Hesam, Ali Mohammad Nia Farid

Mining Science

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2021

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Vol. 28

77-92

EN

Air overpressure (AOp) is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to nearby structures and to cause injuries, to personnel or animals, AOp is one of the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for AOp estimation. The scope of this study is to predict AOp induced by blasting through a novel approach based on the bat algorithm. For this purpose, the parameters of 62 blasting operations were accurately recorded and AOp were measured for each operation. In the next stage, a new empirical predictor was developed to predict AOp. The results clearly showed the superiority of the proposed bat algorithm model in comparison with the empirical approaches.

4

Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors

Alagarsamy Saravanan, Kamatchi Kartheeban, Govindaraj Vishnuvarthanan, Zhang Y. D., Thiyagarajan Arunprasath

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 4

1005--1035

EN

Segregation of tumor region in brain MR image is a prominent task that instantly provides easier tumor diagnosis, which leads to effective radiotherapy planning. For decades together, several segmentation methods for a brain tumor have been presented and until now, enhanced tumor segmentation procedure tends to be a challenging task because, MR images are mostly inbred with varied tumor dimensions of disproportioned boundaries. To address this issue, we develop an improved brain image segmentation technique called BAT based Interval Type-2 Fuzzy C-Means (BAT-IT2FCM) clustering. The BAT algorithm is utilized to find out the optimal cluster location from which the clustering operation by Interval Type-2 Fuzzy C-Means (IT2FCM) is performed. The optimal cluster location pointed/identified by the BAT algorithm helps in easing the clustering operation performed by IT2FCM algorithm, and thereby reducing computational complexity. The efficient outcome from BAT-IT2FCM methodology was affirmed using the performance metrics such as computational time, Peak Signal to Noise Ratio, Mean Squared Error, Jaccard Tanimoto Co-efficient Index and Dice Overlap Index. Also, segmentation results of clinical brain MR images produced by the proposed methodology were evaluated with the support from radiologists (Gold Standard). The suggested BAT based fuzzy related clustering produces sensitivity and specificity values of 98.56 ± 1.2 and 97.67 ± 1.3, respectively, which are better than the existing techniques used for brain image segmentation. Heterogeneous tumor types of different grade levels and tissue structures present in the brain MR slices of three different axes are precisely segmented by the proposed methodology for better visualization of oncologists.

5

Zastosowanie półautomatycznego algorytmu doboru optymalnej liczby i położenia odwiertów wydobywczych

Łętkowski P.

Nafta-Gaz

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2018

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R. 74, nr 8

598--605

PL

Artykuł poświęcono zastosowaniu tzw. algorytmu nietoperza do rozwiązania problemu określenia optymalnej liczby i położenia odwiertów wydobywczych. W procesie optymalizacji jako funkcję celu wykorzystano bieżącą wartość netto (ang. net present value – NPV). Testy zbudowanego algorytmu przeprowadzono na przykładzie modelu symulacyjnego złoża PUNQ-S3, dostępne- go na zasadach open source. Zastosowany algorytm został wyposażony w dodatkowe mechanizmy zwiększające jego efektywność: mechanizm próbkowania sześcianu łacińskiego (ang. Latin hypercube sampling – LHS) oraz mechanizm eliminowania położeń odwiertów poza modelem. Przeprowadzone testy wskazują na bardzo dobrą zbieżność zbudowanego algorytmu w procesie optymalizacji.

EN

The article is devoted to the application of the so-called bat algorithm to solve the problem of determining the optimum number and location of production wells. This algorithm was proposed by Yang in 2010, and since then has been successfully used in solving both theoretical and practical optimization problems. The method belongs to a group of swarm optimization methods and in searching for the best solution, the algorithm uses a mechanism of echolocation, similar to the one used by a herd of bats. The current net present value (NPV) was used as a target function in the optimization process. The algorithm was tested on the example of the simulation model of the PUNQ-S3 reservoir available on an OpenSource basis. The applied algorithm was equipped with additional mechanisms increasing its effectiveness: Latin Hypercube Sampling (LHS) algorithm and the mechanism eliminating the locations of wells outside the operational area of the model. The first of the applied improvements ensures a better starting point for the proper optimization process, which significantly improves the convergence of the whole algorithm. The latter mechanism solves a problem specific to the issue in question.

6

Zastosowanie algorytmu BAT w optymalizacji obliczeń adaptacyjnego regulatora stanu układu dwumasowego

Kamiński M.

Przegląd Elektrotechniczny

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2017

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R. 93, nr 1

300--304

PL

W publikacji analizowany jest regulator stanu, którego parametry podlegają adaptacji zgodnie z regułą Widrowa-Hoffa. Stały współczynnik wykorzystywany przy wyznaczaniu poprawek regulatora stanu wyznaczono za pomocą algorytmu BAT. Sterowanym obiektem jest układ dwumasowy. Przedstawiono analizę właściwości dynamicznych struktury sterowania, wykonano badania dla znamionowych oraz zmodyfikowanych parametrów obiektu, a także porównano działanie klasycznego oraz adaptacyjnego regulatora stanu. Zaprojektowany regulator zaimplementowano w karcie dSPACE1103, a następnie przeprowadzono testy na stanowisku laboratoryjnym.

EN

In article adaptive state space controller is analyzed. Parameters are recalculated according to Widrow-Hoff rule. Inside adaptation algorithm, the constant value of learning rate is selected using BAT algorithm. The plant used in control structure is two-mass system. Dynamical properties of proposed controller are considered. Results are prepared for nominal and disturbed parameters of the plant. Comparison between classical and adaptive controller is also presented. Designed controller has been implemented in dSPACE1103 card, then experiment was prepared.

7

Bat optimization based neuron model of stochastic resonance for the enhancement of MR images

Singh M., Verma A., Sharma N.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

124--134

EN

Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.

8

A comparative study on multi-swarm optimisation and bat algorithm for unconstrained non linear optimisation problems

Baidoo E., Opoku Oppong S

Applied Computer Science

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2016

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Vol. 12, no 4

59--77

EN

A study branch that mocks-up a population of network of swarms or agents with the ability to self-organise is Swarm intelligence. In spite of the huge amount of work that has been done in this area in both theoretically and empirically and the greater success that has been attained in several aspects, it is still ongoing and at its infant stage. An immune system, a cloud of bats, or a flock of birds are distinctive examples of a swarm system. In this study, two types of meta-heuristics algorithms based on population and swarm intelligence - Multi Swarm Optimization (MSO) and Bat algorithms (BA) – are set up to find optimal solutions of continuous non-linear optimisation models. In order to analyze and compare perfect solutions at the expense of performance of both algorithms, a chain of computational experiments on six generally used test functions for assessing the accuracy and the performance of algorithms, in swarm intelligence fields are used. Computational experiments show that MSO algorithm seems much superior to BA.