Sine Cosine Algorithm Algorithm Research Articles

Synthesis of concentric circular antenna array for reducing the sidelobe level by employing sine cosine optimization algorithm

AbstractThe sine cosine algorithm (SCA), a meta‐heuristic optimization method, is used in this study to provide a precise linear and elliptical antenna array design for synthesizing the ideal far‐field radiation pattern in the fifth‐generation (5G) communication spectrum. The wireless communication system will undergo dramatic changes thanks to the forthcoming 5G technology, which offers exceptionally high data rates, increased capacity, reduced latency, and outstanding service quality. The most important component of 5G communications is an accurate antenna array design for an optimum far‐field radiation pattern synthesis with a suppressed sidelobe level (SLL) value and half power beam width (HPBW). While long‐distance communication necessitates a low HPBW, the entire side lobe area needs a suppressed SLL to prevent interference. The SCA is used in this case to the optimal feeding currents applied to each array member in the design examples of the concentric circular antenna arrays (CCAA) discussed in this article. It shows the litheness and attainment of the propound algorithm named SCA, chosen CCAAs with three rings and varying amounts of components or antenna array sets those are stated as follows: Set I (4, 6, 8 elements), Set II (8, 10, 12 elements), Set III (6, 12, 18 elements), Set IV (8, 14, 20 elements) with and without the center element are amalgamate. Apply the PSO, Jaya, and SCA optimization algorithms for all four Sets of antenna arrays and compare the attained results; the SLL values achieved by the SCA technique are contrasted with those of other current optimization techniques. The outcomes of all examinations reveal that the SCA algorithm achieved a superior SLL reduction over other optimization techniques.

A Modified Sine Cosine Algorithm for Numerical Optimization

Sine cosine algorithm (SCA) is a recently developed meta-heuristic method based on the characteristics of sine and cosine functions. However, SCA algorithm may suffer premature convergence in solving complex optimization problems. To mitigate this limitation, a modified SCA (MSCA) is developed to achieve an effective balance between exploration and exploitation. Specifically, the conversion parameter can be configured adaptively by using the individual fitness information in the evolution process to guide the individual search behavior. After that, an inertia weight is embedded in the position updating equation to further improve the search accuracy and accelerate the convergence speed. The performance of MSCA algorithm is investigated on 13 benchmark functions and the chaotic time series prediction problem. Experimental results demonstrate that MSCA algorithm is more effective than the other optimization methods.

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

The purpose of this paper is to demonstrate the superiority of the Capuchin Search Algorithm (CapSA), a metaheuristic, in competitive environments and its advantages in optimizing engineering design problems. To achieve this, the CEC 2019 function set was used. Due to the challenging characteristics of the CEC 2019 function set in reaching a global solution, it effectively showcases the algorithm's quality. For this comparison, sea-horse optimizer (SHO), grey wolf optimizer (GWO), sine-cosine algorithm (SCA), and smell agent optimization (SAO) were chosen as current and effective alternatives to the CapSA algorithm. Furthermore, the gear train design problem (GTD) was selected as an engineering design problem. In addition to the CapSA algorithm, a hybrid of SCA and GWO algorithm (SC-GWO) and genetic algorithm (GA) were chosen as alternatives for optimizing this problem. The performance superiority and optimization power of the CapSA algorithm were assessed using statistical metrics and convergence curves, then compared with alternative algorithms. Experimental results conclusively demonstrate the significant effectiveness and advantages of the CapSA algorithm.

Compressive strength evaluation of ultra-great workability concrete by creating hybridized regression analysis

In the present article, the applicability of proposing hybridized regression evaluation on the ultra-great workability concrete ( UGWC ) was assessed with the goal of declining the assigned time and experimental works. UGWC can be included in several by-products or physical elements. For proposing suggested models, several hybridized regressions were produced, in which the best values of principal attributes of support vector regression (SVR) were clarified by metaheuristic methods such as sine cosine algorithm (SCA), Imperialist competitive algorithm (ICA), called ICA-SVR, and SCA-SVR. The criteria supply that both hybridized ICA-SVR and SCA-SVR models conclude justifiable ability in estimation outline. Taking into account integrated models, the ICA-SVR results in the weakest accuracy compared to SCA-SVR, with R 2 and VAF accounting for 0.9098, and 90.66%, respectively. The highest and most incredible performance owned by the SVR model hybridized with the SCA algorithm, where it was able to receive the largest value of R 2 and VAF in both training and testing datasets, and the smallest value of RMSE , and MAE in both training and testing datasets. MAE value in the training and testing phase for the ICA-SVR model is roughly fourfold and six times than SCA-ANFIS, which depicts the wonderful performance of SCA-SVR in the estimation system. So, the hybridized SCA-SVR can receive the highest precision in comparison with ICA-SVR as well as the published essay ( R 2 =0.801).

Distributed photovoltaic short‐term power forecasting using hybrid competitive particle swarm optimization support vector machines based on spatial correlation analysis

AbstractIn order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spatial correlation (SC), for realizing short‐term PV power prediction tasks. Firstly, the spatial correlation analysis is conducted on the distributed PV stations. The k‐means clustering method based on morphological similarity distance improvement and mutual information function is used to select the best reference station and the best delay, which generates strongly correlated PV sequences. Then, a hybrid algorithm of particle swarm optimization (PSO) and sine cosine algorithm (SCA) in a competitive framework (HCPSO) is proposed, aiming to fuse the fast convergence capability of PSO algorithm with the global search capability of SCA algorithm, while enabling the algorithm to effectively handle high‐dimensional optimization problems based on a competitive mechanism. Finally, the HCPSO algorithm is combined with SVM algorithm, which expands the applicable scenarios of the SVM model and effectively improves the accuracy of PV short‐term prediction.

The Optimum Design of Scanned Linear Antenna Array Using Sine Cosine Optimization Algorithm

In this article, the side lobe level (SLL) of the radiation pattern is reduced, and the first null beam width (FNBW) is kept constant by synthesizing symmetric scanning Linear Antenna Arrays (LAA), which is done by considering excitation amplitude as the optimization parameter. A Sine cosine algorithm (SCA) is used to achieve this objective. Three different case studies are illustratedin this article to show the effectiveness of SCA in LAA optimization. The results obtained show that the SCA algorithm performs better than Firefly Algorithm (FA), Symbiotic Organisms Search (SOS), and hybrid optimization algorithm based on Grasshopper Optimization Algorithm (GOA) and Antlion Optimization (ALO)

Breast cancer detection from histopathological image dataset using hybrid convolution neural network

Cancer of the breast is a deadly disease that can take a person’s life in many different ways. Predicting breast cancer at an early stage is crucial in the fight to end the disease. The usage of deep learning and blockchain technology has been implemented with the intention of integrating optimal prediction with clinical diagnostics and protecting private health information. Patients’ medical records are encrypted and stored in the blockchain for maximum safety. As a result, a large portion of time and energy is spent on feature engineering, a tedious process that requires prior expert domain knowledge of the data to develop effective features, and is crucial to the success of most conventional classification systems. Deep learning, on the other hand, can arrange the discriminative information in the data without the need for a domain expert to develop feature extractors. The research community and industry have paid attention to deep, feedforward networks like convolutional neural networks (CNNs) because of their empirical results in areas including speech recognition, signal processing, object recognition, natural language processing, and transfer learning. For the best breast cancer prediction, a new method called a “Hybrid CNN” combining the Sine Cosine Algorithm (SCA) with a transfer learning algorithm has been presented. Mini-batch size and drop-out rate are just two of the factors that the SCA algorithm may fine-tune. To stop the model from overfitting, we employ a transfer learning strategy. The hyperparameters found using sine cosine algorithm is used in Visual geometry Group (VGG 16) architecture. ImageNet is used to pretrain the network and last three convolutional layers are trained using transfer learning. The integration of sine cosine algorithm and transfer learning together increases the accuracy thereby preventing the model from overfitting. The experimentation is performed in Google Colab and the proposed Hybrid CNN is compared with existing methodologies such as K-NN, SVM. Also, the proposed Hybrid CNN is compared with CNN without transfer learning and CNN without SCA. The metrics taken into account for comparison are accuracy, sensitivity, specificity, F-score. The proposed Hybrid CNN achieves 96.9% accuracy that shows the effectiveness of the integration of SCA and transfer learning.

Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection

Abstract The sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.

An adaptive quadratic interpolation and rounding mechanism sine cosine algorithm with application to constrained engineering optimization problems

The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA.

Improved sine algorithm for global optimization

An improved sine cosine algorithm (abbreviated MSA) is presented to overcome the disadvantages of the sine cosine algorithm (SCA), such as its low accuracy, premature convergence, and slow local convergence. We changed the method for setting the conversion parameters of the SCA algorithm from a linear decline into a nonlinear decline in order to optimize the timing of global exploration and local exploration. In order to improve the convergence accuracy and to increase the convergence speed of the SCA, an inertia weight is introduced in the position update equation. Ten high-dimensional complex benchmark functions are simulated with five improved SCA algorithms, under the same particle numbers and maximum iteration times. According to our experiments, the MSA algorithm is not only much better than two of the improved SCA algorithms in optimization accuracy but also better than the other three improved SCA algorithms in terms of stability.

WOASCALF: A new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems

In recent years, researchers have been focused on solving optimization problems in order to determine the global optimum. Increasing the dimension of a problem increases its computational cost and complexity as well. In order to solve these types of problems, metaheuristic algorithms are used. The whale optimization algorithm (WOA) is one of the most well-known algorithms based on whale hunting behavior. In this paper, the WOA algorithm is combined with the Sine Cosine Algorithm (SCA), which is based on the principle of trigonometric sine-cosine. The WOA algorithm has superior performance in the exploration phase in contrast with the exploitation phase, whereas the SCA algorithm has weaknesses in the exploitation phase. The levy flight distribution has been used in the hybrid WOA and SCA algorithm to improve these deficiencies. This study introduced a novel hybrid algorithm named WOASCALF. In this algorithm, the search agents' position updates are based on a hybridization of the WOA, SCA, and levy flight. Each of these metaheuristic algorithms has reasonable performance, however, the Levy distribution caused small and large distance leaps in each phase of the algorithm. Thus, it is possible for the appropriate search agent to move in different directions of the search space. The performance of the WOASCALF has been evaluated by the 23 well-known benchmark functions and three real-world engineering problems. The result analysis demonstrates that the exploration ability of WOASCALF has strong superiority over other compared algorithms.

Transformer Fault Diagnosis Based on an Improved Sine Cosine Algorithm and BP Neural Network

Background: The operation state evaluation and fault location of the transformer is one of the technical bottlenecks restricting the safe power grid operation. Methods: A hybrid intelligent method based on the Improved Sine Cosine Algorithm and BP neural network (ISCA-BP) is developed to improve the accuracy of transformer fault diagnosis. First, the cloud model is introduced into the Sine Cosine Algorithm (SCA) to determine the conversion parameter of each individual to balance the global search and local exploitation capabilities. After that, six popular benchmark functions are used to evaluate the effectiveness of the proposed algorithm. Finally, based on the dissolved gas analysis technology, the improved SCA algorithm is employed to find the optimal weight and threshold parameters of the BP neural network, and the transformer fault classification model is established. Results: Simulation results indicate that the improved SCA algorithm exhibits strong competitiveness. Furthermore, compared with the BP neural network optimized by the Sine Cosine Algorithm (SCA-BP) and BP neural network, the ISCA-BP method can significantly improve the diagnostic accuracy of transformer faults. Conclusion: The proposed intelligent method can provide a valuable reference idea for transformer fault classification.

EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing

This work aims to improve EEG signal binary and multiclass classification for real-time BCI applications. Therefore, our paper discusses the results of a new real-time approach that was integrated into a complete prediction system, where we proposed a new trick to eliminate the effect of EEG’s non-stationarity nature. This improvement can increase the accuracy from 50% using raw EEG to the order of 90% after preprocessing step in the binary case and from 28% to 78% in the multiclass case. Then, we chose to filter all signals by the proposed bandpass filter automatically optimized using the sine cosine algorithm (SCA) to find the optimal bandwidth that contains the entire EEG characteristics in beta waves. Moreover, we used a common spatial pattern (CSP) filter to eliminate the correlation between all extracted features. Then, the light gradient boosting machine (LGBM) classifier is also combined with SCA algorithm to build better prediction models. As a result, the outcome system was applied on UCI and PhysioNet datasets to get excellent accuracy values of higher than 99% and 95%, respectively, using the data acquired only from three channels. On the other hand, the related works used all the data acquired from 14 channels to find an accuracy value between 70% and 98.5%, which shows the robustness of our method to improve EEG signal prediction quality.

A fast and accurate sine-cosine MPPT algorithm under partial shading with implementation using arduino board

In this paper, we propose a fast and accurate algorithm for global maximum power point tracking (GMPP) based on the sine-cosine algorithm (SCA) of a photovoltaic system (PV) for various weather changes. The objective of this algorithm is to improve the tracking performance of the MPP controller, for optimal power extraction from a PV system under partial shading conditions (PSC). The performance of the proposed SCA algorithm was tested and compared to other algorithms such as perturb and observe (P&O) and the salp swarm algorithm (SSA) under static and dynamic PSC. The simulation results show the superiority and efficiency of the PV system controlled by the SCA algorithm, which outperforms other algorithms in fast and accurate GMPP tracking, fast response time, accelerated convergence speed, very low initial oscillation, very low MPP steady-state oscillation and PV system efficiency that reaches 99.94% under different weather PSC. Moreover, the proposed SCA algorithm is validated by following the V-cycle validation and verification process by integrating the generated embedded software on an onboard “Arduino MKR ZERO board”, which is used as a digital platform for implement the proposed SCA algorithm.

CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems.

Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho.

A family of circulant megastable chaotic oscillators, its application for the detection of a feeble signal and PID controller for time-delay systems by using chaotic SCA algorithm

Chaotic systems with cyclic symmetry are very rare and have been less discussed in the literature. Similarly, megastable oscillators, which can have a finite or infinite number of coexisting attractors, have also attracted researchers. We propose a class of cyclic symmetry oscillators with the megastable property with infinite coexisting attractors for the first time in the literature. Various dynamical properties of the proposed oscillators are discussed in detail. An application for the detection of a feeble signal by using the proposed circulant megastable oscillator is presented. Since chaotic oscillators are highly sensitive to a tiny change in the parameters or an external input to the oscillator, this property of the proposed oscillator is used for the detection of a feeble signal. Simulated results validate the effectiveness of the proposed application. After that, a new chaotic Sine-Cosine Algorithm (SCA) is developed using the randomness of megastable oscillators. Subsequently, this new chaotic sine-cosine algorithm is used to determine the PID controller parameters of time-delay systems concerning the objective function. As a result, the proposed chaotic sine-cosine algorithm presents better performance for time-delay systems when compared with the available algorithms in the literature.

An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting

Accurate and reliable solar radiation forecasting is of great significance for the management and utilization of solar energy. This study proposes a deep learning model based on Bi-directional long short-term memory (BiLSTM), sine cosine algorithm (SCA) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for solar radiation forecasting. Firstly, the CEEMDAN is applied to decompose the stochastic historical time series into certain periodic intrinsic mode functions (IMFs) and a residual. Secondly, the significant antecedent solar radiation patterns of the decomposed sub-modes are identified via two statistical techniques, namely, the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Thirdly, all the sub-modes are forecasted using the BiLSTM model, and the parameters of the BiLSTM model are optimized using the SCA algorithm. Finally, the forecasted sub-modes are aggregated to generate the final forecasting result. The accuracy of the proposed deep learning model is investigated by applying it in forecasting hourly solar radiation of four real-world datasets over multi-step horizons. Comparative experiments with other seven models demonstrate the effectiveness of the integrated model, the CEEMDAN technique and the SCA algorithm, respectively. The proposed model can obtain higher prediction accuracy than the existing models for all datasets and forecasting horizons.

Advances in Sine Cosine Algorithm: A comprehensive survey

The Sine Cosine Algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance of the SCA against similar algorithms.

Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other meta-heuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

A Hybrid Approach Based on SOCP and the Discrete Version of the SCA for Optimal Placement and Sizing DGs in AC Distribution Networks

This paper deals with the problem of the optimal placement and sizing of distributed generators (DGs) in alternating current (AC) distribution networks by proposing a hybrid master–slave optimization procedure. In the master stage, the discrete version of the sine–cosine algorithm (SCA) determines the optimal location of the DGs, i.e., the nodes where these must be located, by using an integer codification. In the slave stage, the problem of the optimal sizing of the DGs is solved through the implementation of the second-order cone programming (SOCP) equivalent model to obtain solutions for the resulting optimal power flow problem. As the main advantage, the proposed approach allows converting the original mixed-integer nonlinear programming formulation into a mixed-integer SOCP equivalent. That is, each combination of nodes provided by the master level SCA algorithm to locate distributed generators brings an optimal solution in terms of its sizing; since SOCP is a convex optimization model that ensures the global optimum finding. Numerical validations of the proposed hybrid SCA-SOCP to optimal placement and sizing of DGs in AC distribution networks show its capacity to find global optimal solutions. Some classical distribution networks (33 and 69 nodes) were tested, and some comparisons were made using reported results from literature. In addition, simulation cases with unity and variable power factor are made, including the possibility of locating photovoltaic sources considering daily load and generation curves. All the simulations were carried out in the MATLAB software using the CVX optimization tool.