scholarly journals Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

2021 ◽  
Vol 11 (12) ◽  
pp. 5620
Author(s):  
Jorge M. Cruz-Duarte ◽  
José C. Ortiz-Bayliss ◽  
Ivan Amaya ◽  
Nelishia Pillay
Keyword(s):  
Simulated Annealing ◽  
Technological Progress ◽  
Computational Cost ◽  
Population Based ◽  
Diverse Population ◽  
Natural Phenomena ◽  
High Level ◽  
Solver Performance ◽  
Low Computational Cost ◽  
Simple Heuristics

Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.

scholarly journals ESIKE: An efficient and secure internet key  exchange protocol

2021 ◽  
Author(s):  
marwa ahmim ◽  
Ahmed Ahmim ◽  
Mohamed amine Ferrag ◽  
Nacira ghoualmi-zine ◽  
Leandros Maglaras
Keyword(s):  
Computational Cost ◽  
Key Exchange ◽  
Key Exchange Protocol ◽  
Man In The Middle ◽  
Key Exchange Protocols ◽  
Perfect Forward Secrecy ◽  
High Level ◽  
Use Of Internet ◽  
Low Computational Cost ◽  
Internet Key Exchange

Abstract The use of Internet key exchange protocols in IP Security architecture and in IoT environments has vulnerabilities against various malicious attacks and affects communication efficiency. To address these weaknesses, we propose a novel efficient and secure Internet key exchange protocol (ESIKE), which achieves a high level of security along with low computational cost and energy consumption. ESIKE achieves perfect forward secrecy, anonymity, known-key security and untraceability properties. ESIKE can resist several attacks, such as, replay, DoS, eavesdropping, man-in-the-middle and modification. In addition, the formal security validation using AVISPA tools confirms the superiority of ESIKE in terms of security.

scholarly journals Pipeline leak detection using the multiple signal classification-like method

2020 ◽  
Vol 22 (5) ◽  
pp. 1321-1337
Author(s):  
Juan Li ◽  
Ying Wu ◽  
Changgang Lu
Keyword(s):  
Computational Cost ◽  
Leak Detection ◽  
Signal Classification ◽  
Accurate Identification ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Leak Location ◽  
High Level ◽  
Low Computational Cost ◽  
Leakage Location

Abstract Leakages in pipelines can cause severe hazards to industries, the environment and people. For the purpose of an accurate identification of the leakage location, a transient-based leakage detection method using multiple signal classification (MUSIC)-like is applied to this paper. The localization is achieved by a one-dimensional search of leak location along the pipe, which means it involves low computational cost. The performance of the MUSIC-Like method in the cases of a single leak and multiple leaks is discussed by comparison with three spectral-based methods. In the single-leak case, the MUSIC-like algorithm provides precise localization estimation even for a high level of noise. For the multiple-leak case, the MUSIC-like method is superior to the other three methods. It is capable of identifying all leaks where the leak-to-leak distance is less than half the shortest probing wavelength. Therefore, the MUSIC-like method has an excellent performance in leak detection and location.

scholarly journals Optimal hydropower operation of multi-reservoir systems: hybrid cellular automata-simulated annealing approach

2020 ◽  
Vol 22 (5) ◽  
pp. 1236-1257
Author(s):  
Mohamad Azizipour ◽  
Ali Sattari ◽  
Mohammad Hadi Afshar ◽  
Erfan Goharian ◽  
Samuel Sandoval Solis
Keyword(s):  
Simulated Annealing ◽  
Cellular Automata ◽  
Large Scale ◽  
Computational Cost ◽  
Population Based ◽  
Benchmark Problems ◽  
Hybrid Cellular Automata ◽  
Reservoir Systems ◽  
The Usa ◽  
Hydropower Operation

Abstract Hydropower operation of multi-reservoir systems is very difficult to solve mostly due to their nonlinear, nonconvex and large-scale nature. While conventional methods are long known to be incapable of solving these types of problems, evolutionary algorithms are shown to successfully handle the complexity of these problems at the expense of very large computational cost, particularly when population-based methods are used. A novel hybrid cellular automata-simulated annealing (CA-SA) method is proposed in this study which avoids the shortcomings of the existing conventional and evolutionary methods for the optimal hydropower operation of multi-reservoir systems. The start and the end instances of time at each operation period is considered as the CA cells with the reservoir storages at these instances are taken as the cell state which leads to a cell neighborhood defined by the two adjacent periods. The local updating rule of the proposed CA is derived by projecting the objective function and the constraints of the original problem on the cell neighborhoods represented by an optimization sub-problem with the number of decision variables equal to the number of reservoirs in the system. These sub-problems are subsequently solved by a modified simulated annealing approach to finding the updated values of the cell states. Once all the cells are covered, the cell states are updated and the process is iterated until the convergence is achieved. The proposed method is first used for hydropower operation of two well-known benchmark problems, namely the well-known four- and ten-reservoir problems. The results are compared with the existing results obtained from cellular automata. Genetic algorithm and particle swarm optimization indicating that the proposed method is much more efficient than existing algorithms. The proposed method is then applied for long-term hydropower operation of a real-world three-reservoir system in the USA, and the results are presented and compared with the existing results.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

scholarly journals Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 149
Author(s):  
Petros Zervoudakis ◽  
Haridimos Kondylakis ◽  
Nicolas Spyratos ◽  
Dimitris Plexousakis
Keyword(s):  
Query Optimization ◽  
Query Language ◽  
Computational Cost ◽  
Continuous Queries ◽  
Continuous Query ◽  
Query Rewriting ◽  
Query Evaluation ◽  
Clear Separation ◽  
Complete Dataset ◽  
High Level

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals Numerical simulation and experimental validation of cavitating flow in a multi-hole diesel fuel injector

2021 ◽  
pp. 146808742199863
Author(s):  
Aishvarya Kumar ◽  
Ali Ghobadian ◽  
Jamshid Nouri
Keyword(s):  
Computational Cost ◽  
Cavitating Flow ◽  
Field Analysis ◽  
Navier Stokes ◽  
Fuel Injector ◽  
Fluid Behavior ◽  
Flow Field Analysis ◽  
Charged Couple Device ◽  
Biodiesel Fuels ◽  
High Level

This study assesses the predictive capability of the ZGB (Zwart-Gerber-Belamri) cavitation model with the RANS (Reynolds Averaged Navier-Stokes), the realizable k-epsilon turbulence model, and compressibility of gas/liquid models for cavitation simulation in a multi-hole fuel injector at different cavitation numbers (CN) for diesel and biodiesel fuels. The prediction results were assessed quantitatively by comparison of predicted velocity profiles with those of measured LDV (Laser Doppler Velocimetry) data. Subsequently, predictions were assessed qualitatively by visual comparison of the predicted void fraction with experimental CCD (Charged Couple Device) recorded images. Both comparisons showed that the model could predict fluid behavior in such a condition with a high level of confidence. Additionally, flow field analysis of numerical results showed the formation of vortices in the injector sac volume. The analysis showed two main types of vortex structures formed. The first kind appeared connecting two adjacent holes and is known as “hole-to-hole” connecting vortices. The second type structure appeared as double “counter-rotating” vortices emerging from the needle wall and entering the injector hole facing it. The use of RANS proved to save significant computational cost and time in predicting the cavitating flow with good accuracy.

scholarly journals A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 23
Author(s):  
Duy-Anh Nguyen ◽  
Xuan-Tu Tran ◽  
Francesca Iacopi
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Training Algorithms ◽  
Current State ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
High Level ◽  
Event Based ◽  
Hardware Platforms

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

Inverse Association Between Health Literacy and Obesity Among Children in Greece: A School-Based, Cross-Sectional Epidemiological Study

2021 ◽  
pp. 109019812098294
Author(s):  
Aikaterini Kanellopoulou ◽  
Venetia Notara ◽  
George Antonogeorgos ◽  
Maria Chrissini ◽  
Andrea Paola Rojas-Gil ◽  
...  
Keyword(s):  
Health Literacy ◽  
Weight Status ◽  
Dietary Habits ◽  
Population Based ◽  
Activity Levels ◽  
Inverse Association ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Body Weight Status ◽  
High Level

Children’s health literacy is a crucial pillar of health. This study is aimed to examine the association between health literacy and weight status among Greek schoolchildren aged 10 to 12 years old. A population-based, cross-sectional observational study enrolling 1,728 students (795 boys), aged 10 to 12 years old, was conducted during school years 2014–2016. A health literacy index (range 0-100) was created through an item response theory hybrid model, by combining a variety of beliefs and perceptions of children about health. The mean health literacy score was 70.4 (±18.7). The majority of children (63.8%) had a “high” level (i.e., >67/100) of health literacy, 30.5% had a “medium” level (i.e., 34–66/100) of health literacy, while a small proportion of children (5.7%) had a “low” level (i.e., <33/100). Girls exhibited a higher level of health literacy than boys (71.7 ± 18.3 vs. 68.8 ± 19.1, p < .01). Regarding body weight status, 21.7% of children was overweight and 5.0% was obese. Linear regression models showed that the health literacy score was inversely associated with children’s body mass index (regression coefficient [95% CI]: −0.010 [−0.018, −0.001]), after adjusting for dietary habits, physical activity levels, and other potential confounders. Health literacy seems to be a dominant characteristic of children’s weight status; therefore, school planning, as well as public health policy actions should emphasize on the ability of children’s capacity to obtain, process, and understand basic health information.

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