An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems

A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby extending the lifetime of this energy-constrained device.This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously.In this paper,we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks.It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power.A corresponding system model is summarized based on the scenario and existing theoretical works.The minimum throughputmaximizing object is then formulated as an optimization problem.As swarm intelligence algorithms are utilized effectively in numerous fields,this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms.This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem,as joint time and energy optimization have two sets of variables.The proposed method performs well in terms of stability and duration.Finally,performance is evaluated through numerical experiments.Simulation results demonstrate that the proposed method performs admirably in the given scenario.

Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms

A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model.

Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

Currently,e-learning is one of the most prevalent educational methods because of its need in today’s world.Virtual classrooms and web-based learning are becoming the new method of teaching remotely.The students experience a lack of access to resources commonly the educational material.In remote loca-tions,educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations.The objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning applications.The proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best servers.For optimal server selection,the Rider Optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing techniques.The proposed Rider Optimization Algorithm is compared to Par-ticle Swarm Optimization(PSO),Genetic Algorithm(GA)and Firefly Algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user requests.Thus the proposed method outweighs the conventional techniques by its enhanced performance over them.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

In Intelligent Transportation Systems(ITS),controlling the trafficflow of a region in a city is the major challenge.Particularly,allocation of the traffic-free route to the taxi drivers during peak hours is one of the challenges to control the trafficflow.So,in this paper,the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized.Initially,the hotspots in a region are clustered using the density-based spatial clustering of applications with noise(DBSCAN)algorithm tofind the hot spots at the peak hours in an urban area.Then,the optimal route is allocated to the taxi driver to pick up the customer in the hotspot.Before allocating the optimal route,each route between the taxi driver and the hot spot is mapped to the number of taxi drivers.Among the map function,the optimal map is selected using the rain opti-mization algorithm(ROA).If more than one map function is obtained as the opti-mal solution,the map between the route and the taxi driver who has done the least number of trips in the day is chosen as thefinal solution This optimal route selec-tion leads to control of the trafficflow at peak hours.Evaluation of the approach depicts that the proposed trafficflow control scheme reduces traveling time,wait-ing time,fuel consumption,and emission.

Development of Fixture Layout Optimization for Thin-Walled Parts:A Review

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

A New Double Layer Multi-Secret Sharing Scheme

Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it.Visual Secret Sharing(VSS)is an encryption method in which the secret message is split into at least two trivial images called’shares’to cover it.However,such message are always targeted by hackers or dishonest members who attempt to decrypt the message.This can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of,by the trusted party.Hence,in this paper,an optimal and secure double-layered secret image sharing scheme is proposed.The proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second layer.In first layer,Genetic Algorithm(GA)is applied to find the optimal threshold value based on the randomness of the created shares.Then,in the second layer,a novel design of universal share-based secret share creation method is proposed.Finally,Opposition Whale Optimization Algorithm(OWOA)-based optimal key was generated for rectange block cipher to secure each share.This helped in producing high quality reconstruction images.The researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 respectively.The average PSNRwas less(49.134475)and average MSE was high(1)in case of existing methods.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.