A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

The essential purpose of radar is to detect a target of interest and provide information concerning the target’s location,motion,size,and other parameters.The knowledge about the pulse trains’properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance.A low autocorrelation binary sequence(LABS)is a complex combinatorial problem.The main problems of LABS are low Merit Factor(MF)and shorter length sequences.Besides,the maximum possible MF equals 12.3248 as infinity length is unable to be achieved.Therefore,this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm(HMSCACSA)using Inverse Filtering(IF)and clipping method to achieve better results.The proposed algorithms,LABS-IF and HMSCACSA-IF,achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237,respectively,where the optimal solutions belong to the skew-symmetric sequences.The MF outperformed up to 24.335%and 2.708%against the state-of-the-art LABS heuristic algorithm,xLastovka,and Golay,respectively.These results indicated that the proposed algorithm’s simulation had quality solutions in terms of fast convergence curve with better optimal means,and standard deviation.

A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure.Sine Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&Cosine.However,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal regions.In this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive weight.The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities.To evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are used.Moreover,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA.The results show that RDSCA has competitive results with other metaheuristics algorithms.

Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm

Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization problems.To ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this paper.The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima.Furthermore,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO.Finally,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems respectively.The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to cyberattacks.It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks.Botnet is one of the dreadfulmalicious entities that has affected many users for the past few decades.It is challenging to recognize Botnet since it has excellent carrying and hidden capacities.Various approaches have been employed to identify the source of Botnet at earlier stages.Machine Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection methodology.In spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset.The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)model.The presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter tuning.Initially,the network data is pre-processed to make it compatible for further processing.Besides,RSO algorithm is exploited for effective selection of subset of features.Additionally,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of botnets.Finally,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM model.In order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was conducted.The obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

Modeling and identification of magnetostrictive hysteresis with a modified rate-independent Prandtl-Ishlinskii model

This paper presents a modified rate-independent Prandtl-Ishlinskii （MRIPI） model based on the Fermi-Dirac distri- bution for the asymmetric hysteresis description of magnetostrictive actuators. Generally, the classical Prandtl-Ishlinskii （CPI） model can hardly describe the asymmetric hysteresis. To overcome this limitation, various complex operators have been developed to replace the classical operator. In this study, the proposed MRIPI model maintains the classical operator while a modified input function based on the Fermi-Dirac distribution is presented to replace the classical input function. With this method, the MRIPI model can describe the asymmetric hysteresis of magnetostrictive actuators in a relatively simple mathematic format and has fewer parameters to be identified. A velocity-based sine cosine algorithm （VSCA） is also proposed for the parameter identification of the MRIPI model. To verify the validity of the MRIPI model, experiments are performed and the results are compared with those of the existing modeling methods.

A Large-Scale Scheduling Method for Multiple Agile Optical Satellites

This study investigates the scheduling problem ofmultiple agile optical satelliteswith large-scale tasks.This problem is difficult to solve owing to the time-dependent characteristic of agile optical satellites,complex constraints,and considerable solution space.To solve the problem,we propose a scheduling method based on an improved sine and cosine algorithm and a task merging approach.We first establish a scheduling model with task merging constraints and observation action constraints to describe the problem.Then,an improved sine and cosine algorithm is proposed to search for the optimal solution with the maximum profit ratio.An adaptive cosine factor and an adaptive greedy factor are adopted to improve the algorithm.Besides,a taskmerging method with a task reallocation mechanism is developed to improve the scheduling efficiency.Experimental results demonstrate the superiority of the proposed algorithm over the comparison algorithms.