A Redundant Binary Algorithm for RSA

The normal form and modilied normal form for binary redundant representation are defined. A redundant binary algorithm to compute modular exponentiation for very large integers is proposed. It is shown that the proposed algorithm requires the minimum number of basic operations (modular multiplications) among all possible binary redundant representations.

Bio-Inspired Binary Bees Algorithm for a Two-Level Distribution Optimisation Problem

Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.

A Novel Binary Firefly Algorithm for the Minimum Labeling Spanning Tree Problem

Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems.

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical waste.Medical wastage is not properly managed it will become a hazard to the environment and humans.Managing medical wastage is a major issue in the city,municipalities in the aspects of the environment,and logistics.An efficient supply chain with edge computing technology is used in managing medical waste.The supply chain operations include processing of waste collec-tion,transportation,and disposal of waste.Many research works have been applied to improve the management of wastage.The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and transparency.To over-come these issues,in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical waste.To minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is used.The accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data:A COVID-19 Case Study

Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image processing.Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases.Key to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and treatment.This study makes a small contribution to endoscopic image classification.The proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception.Additionally,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature vectors.The fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)classifier.The Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were used.Performance assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private dataset.This approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

Relative efficiency appraisal of discrete choice modeling algorithms using small-scale maximum likelihood estimator through empirically tailored computing environment

Discrete choice models are widely used in multiple sectors such as transportation, health, energy, and marketing, etc., where the model estimation is usually carried out by using commercial software. Nonetheless, tailored computer codes offer modellers greater flexibility and control of unique modelling situation. Aligned with empirically tailored computing environment, this research discusses the relative performance of six different algorithms of a discrete choice model using three key performance measures： convergence time, number of iterations, and iteration time. The computer codes are developed by using Visual Basic Application （VBA）. Maximum likelihood function （MLF） is formulated and the mathematical relationships of gradient and Hessian matrix are analytically derived to carry out the estimation process. The estimated parameter values clearly suggest that convergence criterion and initial guessing of parameters are the two critical factors in determining the overall estimation performance of a custom-built discrete choice model.

Uncertain bilevel knapsack problem based on an improved binary wolf pack algorithm

To address indeterminism in the bilevel knapsack problem,an uncertain bilevel knapsack problem(UBKP)model is proposed.Then,an uncertain solution for UBKP is proposed by defining thePE Nash equilibrium andPE Stackelberg-Nash equilibrium.To improve the computational efficiency of the uncertain solution,an evolutionary algorithm,the improved binary wolf pack algorithm,is constructed with one rule(wolf leader regulation),two operators(invert operator and move operator),and three intelligent behaviors(scouting behavior,intelligent hunting behavior,and upgrading).The UBKP model and thePE uncertain solution are applied to an armament transportation problem as a case study.

System level test selection based on combinatorial dependency matrix

Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators.The existing models and methods are not suitable for system level test selection.The first problem is the lack of detailed data of the units’fault set and the test set,which makes it impossible to establish a traditional dependency matrix for the system level.The second problem is that the system level fault detection rate and the fault isolation rate(referred to as"two rates")are not enough to describe the fault diagnostic ability of the system level tests.An innovative dependency matrix(called combinatorial dependency matrix)composed of three submatrices is presented.The first problem is solved by simplifying the submatrix between the units’fault and the test,and the second problem is solved by establishing the system level fault detection rate,the fault isolation rate and the integrated fault detection rate(referred to as"three rates")based on the new matrix.The mathematical model of the system level test selection problem is constructed,and the binary genetic algorithm is applied to solve the problem,which achieves the goal of system level test selection.

Fault location of distribution networks based on multi-source information

In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.

Uncertain bilevel knapsack problem and its solution

This paper aims at providing an uncertain bilevel knapsack problem (UBKP) model, which is a type of BKPs involving uncertain variables. And then an uncertain solution for the UBKP is proposed by defining PE Nash equilibrium and PE Stackelberg Nash equilibrium. In order to improve the computational efficiency of the uncertain solution, several operators (binary coding distance, inversion operator, explosion operator and binary back learning operator) are applied to the basic fireworks algorithm to design the binary backward fireworks algorithm (BBFWA), which has a good performance in solving the BKP. As an illustration, a case study of the UBKP model and the P-E uncertain solution is applied to an armaments transportation problem.

Optimal Allocation of a Hybrid Wind Energy-Fuel Cell System Using Different Optimization Techniques in the Egyptian Distribution Network

This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.

Weighting IFT algorithm for off-axis quantized kinoforms of binary objects

The results of experiments on the synthesis of the off-axis quantized kinoforms of binary objects with the use of the weighting iterative Fourier transform （WIFT） algorithm are presented. Kinoforms are registered with a liquid-crystal spatial light modulator （SLM）. A simple procedure to introduce the carrier frequency into the structure of an axial kinoform is proposed. An image reconstructed by an off-axis kinoform is free from the noises with the zero and close frequencies caused by the imperfection of both the phase mode of operation of the SLM and the effects of quantization of the registered phase. Data on the diffraction efficiency are also given.

An Algorithm for Automated Identification of Gust Fronts from Doppler Radar Data

Gust fronts are weak narrow-band echoes of increased reflectivity at the background levels in the low-elevation fields of Doppler radar. An automated approach to gust front detection that relies on the image features of radar observations is presented in this paper. The algorithm is not sensitive to the variations in reflectivity values and gust front widths. The approach includes the following steps. First, a novel local binary with dual-template(LBDT) algorithm is designed as the fundamental algorithm to identify the potential areas of narrow-band echoes. Second,based on the disadvantages of the LBDT algorithm, several modifications are made, including splitting the intersecting lines, connecting the fragments, and filtering the edges and radial interference noise. Third, an optical flow method is used to determine whether a weak narrow-band echo is a gust front according to the prior knowledge that a gust front usually propagates in front of the associated generating storm. The results of experiments show that the proposed method can automatically identify gust fronts with a high probability of detection and a low false alarm rate. The automatic identification of gust fronts is potentially useful for accurate short-term weather forecasting, particularly in the forecasting of storm winds.

Distributed resource scheduling in smart grid with electric vehicle deployment using fireworks algorithm

Global warming and climate change are two key probing issues in the present context.The electricity sector and transportation sector are two principle entities propelling both these issues.Emissions from these two sectors can be offset by switching to greener ways of transportation through the electric vehicle (EV) and renewable energy technologies (RET).Thus,effective scheduling of both resources holds the key to sustainable practice.This paper presents a scheduling scenario-based approach in the smart grid.Problem formulation with dual objective function including both emissions and cost is developed for conventional unit commitment with EV and RET deployment.In this work,the scheduling and commitment problem is solved using the fireworks algorithm which mimics explosion of fireworks in the sky to define search space and the distance between associated sparks to evaluate global minimum.Further,binary coded fireworks algorithm is developed for the proposed scheduling problem in the smart grid.Thereafter,possible scenarios inconventional as well as smart grid are put forward.Following that,the proposed methodology is simulated using a test system with thermal generators.

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative(PID)controllers are being used in the industries for control purposes.It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems.Therefore,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system.This paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient stability.STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM.The controller parameters are also optimized by using soft computing technique for performance improvement.This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose.Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

Novel quantum-inspired firefly algorithm for optimal power quality monitor placement

The application of a quantum-inspired firefly algorithm was introduced to obtain optimal power quality monitor placement in a power system. The conventional binary firefly algorithm was modified by using quantum principles to attain a faster convergence rate that can improve system performance and to avoid premature convergence. In the optimization process, a multi-objective function was used with the system observability constraint, which is determined via the topological monitor reach area concept. The multi-objective function comprises three functions： number of required monitors, monitor over-lapping index, and sag severity index. The effectiveness of the proposed method was verified by applying the algorithm to an IEEE 118-bus transmission system and by comparing the algorithm with others of its kind.

PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.