Improved Sine Cosine Algorithm Research Articles

A novel efficient energy optimization in smart urban buildings based on optimal demand side management

Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings.

Structure Optimization of Holding Poles Based on the Improved Sine Cosine Algorithm

Structure Optimization of Holding Poles Based on the Improved Sine Cosine Algorithm

An Improved Sine Cosine Algorithm for the Day-ahead Microgrid Management in the Presence of Electric Vehicles

An Improved Sine Cosine Algorithm for the Day-ahead Microgrid Management in the Presence of Electric Vehicles

Optimization of SVC Placement and Capacity in the Electric Power System Transmission Networks using Multi-Objective Improved Sine Cosine Algorithm

Current technological developments are in line with the increasing consumption of electrical energy. There is a value of power losses of the electricity transmission process caused by an increase in the value of power losses, to overcome this, SVC (Static VAR Compensator) of the Flexible AC Transmission System (FACTS) can be used. From previous studies, the optimization of SVC placement in the transmission network has not been carried out to get better power losses. This research uses the Improved-Sine Cosine Algorithm (ISCA) that has a different function of r1 compared to the ordinary SCA, in which the use of the ISCA method is able to overcome the weaknesses of the SCA method. The determination of location and capacity can use more than one objective function. From the result, the optimization of SVC placement and capacity is able to reduce the value of power losses by up to 85%.

Improved Sine-cosine Algorithm for the Optimization Design of Truss Structures

Improved Sine-cosine Algorithm for the Optimization Design of Truss Structures

Optimal PID controller for the DC-DC buck converter using the improved sine cosine algorithm

This paper presents an improved Sine Cosine Algorithm (ISCA) towards optimization of a DC-DC buck converter with employment of the proportional-integral-derivative (PID) controller. Limitations of the conventional Sine Cosine Algorithm (SCA) was hereby overcome through two separate alterations which pioneered a synergized employment of nonlinear equation to instrumental mechanism in revising the average location. Primary alteration tackled the issue of local optima by proposed instrumental function towards revision of average location. Secondary alteration then coordinated disproportional exploration and exploitation phases of the conventional SCA by application of a nonlinear equation against the algorithm's decreasing position-updated mechanism. Robustness of the proposed ISCA-PID approach was studied against preceding algorithm-based PID for DC-DC buck converter with respect to their step response, statistics regarding the analyzed objective function, time-domain integral-error performance, reaction to frequency, and resistance to disturbance and parametric uncertainties. Generated findings subsequently uncovered overshadowing efficacy of the proposed method over its algorithmic predecessors towards exceptionally enhanced transitory response of the DC-DC buck converter.

An Automatic Pipe-Routing Algorithm Based on Improved Sine Cosine Algorithm for Complex Space

An Automatic Pipe-Routing Algorithm Based on Improved Sine Cosine Algorithm for Complex Space

Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution

Artificial intelligence (AI) acts as a vital part of enhancing network security using intrusion detection and anomaly detection. These AI-driven approaches have become essential components of modern cybersecurity strategies. Conventional IDS is based on predefined signatures of known attacks. AI improves signature-based detection by automating the signature generation and reducing false positives through pattern recognition. It can automate threat detection and response, allowing for faster reaction times and reducing the burden on human analysts. With this motivation, this study introduces an Improved Sine Cosine Algorithm with a Deep Learning-Enabled Security Solution (ISCA-DLESS) technique. The presented ISCA-DLESS technique relies on metaheuristic-based feature selection (FS) and a hyperparameter tuning process. In the presented ISCA-DLESS technique, the FS technique using ISCA is applied. For the detection of anomalous activities or intrusions, the multiplicative long short-term memory (MLSTM) approach is used. For improving the anomaly detection rate of the MLSTM approach, the fruitfly optimization (FFO) algorithm can be utilized for the hyperparameter tuning process. The simulation value of the ISCA-DLESS approach was tested on a benchmark NSL-KDD database. The extensive comparative outcomes demonstrate the enhanced solution of the ISCA-DLESS system with other recent systems with a maximum accuracy of 99.69%.

Statistical Analysis Based Load Frequency Control of multi-area system with Nonlinearities using 2-DOF-PID controller with Application of Improved Sine-Cosine Algorithm optimizer

Statistical Analysis Based Load Frequency Control of multi-area system with Nonlinearities using 2-DOF-PID controller with Application of Improved Sine-Cosine Algorithm optimizer

Text Document Clustering Approach by Improved Sine Cosine Algorithm

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

A collaborative central domain adaptation approach with multi-order graph embedding for bearing fault diagnosis under few-shot samples

Effective fault diagnosis is a prerequisite for ensuring the safe, stable and long-term operation of many rotating machinery. With the rapid development of measurement, sensor and computing technologies, measurement data presents a high-dimensional and massive distribution. This makes the valuable fault information in samples sparse. Moreover, industrial data can only present the distribution state of few-shot unlabeled information. In addition, the vibration signal of bearing faults contains noise interference, leading to poor stability and low efficiency of most models. In this study, we propose an approach for rolling bearing faults diagnosis under few-shot samples. It consists of a multi-order graph embedding stacked denoising auto encoder optimized by an improved sine–cosine​ algorithm (MGE-ISCA-SDAE) and a collaborative central domain adaptation (CCDA). First, a multi-order graph embedding model and an ISCA-based strategy are designed to improve the SDAE, thereby improving the feature extraction effect. To overcome the sparseness of valuable information, we design a CCDA model that learns the fault features using the labeled samples. Subsequently, it is transferred to the target domain of few-shot labeled samples for adaptation. Finally, the intelligent diagnosis is achieved under few-shot samples. We conduct experiments with four datasets. The results show that the MGE-ISCA-SDAE can extract the time–frequency high-level fault features. The CCDA model can transfer the fault samples well. When there are fewer fault samples, our approach has outstanding advantages.

Simultaneous network reconfiguration and DG allocation in radial distribution networks using arithmetic optimization algorithm

AbstractThis article introduces a newly suggested meta‐heuristic arithmetic optimization algorithm (AOA) to reorganize the distribution network with the simultaneous distribution of distributed generators (DGs). The algorithm is designed to be flexible with a new approach that employs the scattering behavior of arithmetic operators in mathematics to discover the solution space more proficiently throughout the path of iterations. The usefulness of the AOA is demonstrated on 33‐bus, and 69‐bus radial distribution networks (RDNs) with network reconfiguration (NR) in the presence of DGs. The main objective of NR and effective DG arrangement in RDNs is to truncate the true power loss, reduce the voltage fluctuation, and voltage stability index enlargement. In order to increase the efficacy of the RDN, a multi‐objective function is created in view of active power loss, voltage deviation, and voltage stability index of the system with appropriate strategy without destroying the system restrictions. To illustrate the effectiveness of the RDN, four separate cases of NR and DG employment have been carried out. Four cases namely, base case (case‐1), reconfiguration (case‐2), simultaneous reconfiguration and DG installation with a unity power factor (case‐3), and simultaneous reconfiguration and DG installation with 0.95 lagging power factor (case‐4) are explored. The projected technique gives better results than other renowned methods, namely, fireworks algorithm (FWA), harmony search algorithm (HSA), refined genetic algorithm (RGA), and improved sine‐cosine algorithm (ISCA) strategy in view of 33, 69‐bus RDN at three dissimilar load conditions and its supremacy is confirmed.

An improved sine cosine algorithm with heterogeneous subpopulations for global optimization and fractional order PID controller design

An improved sine cosine algorithm with heterogeneous subpopulations for global optimization and fractional order PID controller design

A novel method for calculating the contribution rates of economic growth factors

<abstract><p>The common production functions include the Leontief production function, the Cobb-Douglas (C-D) production function, the constant elasticity of substitution (CES) production function, the variable elasticity of substitution (VES) production function and so on. With different elasticity of substitution of factor, the production functions have different ranges of applications. In the production functions, the C-D production function is used the most widely because of its simple form, while the CES production function and the VES production function have limitations in applications due to their complicated forms. However, the C-D production function has the elasticity of substitution of factors of 1, and the CES production function has the elasticity of substitution of factors which is not 1 but a constant, so the two production functions both have limitations in applications. The VES production function with the variable elasticity of substitution is more practical in some application cases. This paper studies the applications of the VES production function model and gives a method of calculating the contribution rates of economic growth factors scientifically. As for the parameter estimation of the model, this paper gives an improved Sine Cosine Algorithm (SCA) to enhance the convergence rate and precision. Finally, the paper makes an empirical analysis on the contribution rates of economic growth factors of Shanghai City, China, using the method proposed.</p></abstract>

Text Classification using Improved IWO-HAN

In this paper, a novel Improved Invasive Weed Optimization-based Hierarchical Attention Network (IIWO-HAN) is developed to achieve text classification. As for huge datasets, automatic labelling is required to get useful insights. Thus, the text classification becomes very important. IIWO has the quality of randomness and imitating compatibility of weeds colony and HAN makes use of levelled document structure. Hence, these two are integrated together to achieve text classification. Then, the proposed method has been analysed based on five popular parameters namely, Accuracy, Precision, TPR, TNR and FNR. For this purpose, three datasets: Reuters dataset, 20-Newsgroup dataset and Self-created dataset have been utilized where Reuters dataset, 20- Newsgroup dataset are standard datasets and the Self-created dataset consists of 5000 documents comprising of abstracts taken from various reputed journals. Further, the proposed methodology has been compared with an existing Improved Sine Cosine Algorithm (ISCA). It is found that IIWO-HAN results in 81.3%, 86.5%, 84.1%, 89.4% and 12.4% for TPR, TNR, Accuracy, Precision and FNR respectively and achieves better performance as compared to existing ISCA which gives 78.1%, 79.4%, 79.4%, 75.6%, 17.8% as TPR, TNR, Accuracy, Precision and FNR respectively for Reuters dataset. For 20- Newsgroup dataset, IIWO- HAN provides the TPR, TNR, Accuracy, Precision and FNR of 87.5%, 88.4%, 87.1%, 91.4% and 12.4% respectively and 84.1%, 86.5%, 89.4%, 92.4% and 15.8% as TPR, TNR, Accuracy, Precision and FNR respectively for Self-created dataset. It is found that the Precision of 92.47% and Accuracy of 89.4% is achieved using self-created dataset at training data value of 90% which is clearly better than Precision of 77.6% and Accuracy of 82.3% for existing ISCA.

Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

The Internet of Things (IoT) offers a new era of connectivity, which goes beyond laptops and smart connected devices for connected vehicles, smart homes, smart cities, and connected healthcare. The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users. With the increasing use of multimedia in communications, the content security of remote-sensing images attracted much attention in academia and industry. Image encryption is important for securing remote sensing images in the IoT environment. Recently, researchers have introduced plenty of algorithms for encrypting images. This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption (ISCACE-RSI) technique in IoT Environment. The proposed model follows a three-stage process, namely pre-processing, encryption, and optimal key generation. The remote sensing images were preprocessed at the initial stage to enhance the image quality. Next, the ISCACE-RSI technique exploits the double-layer remote sensing image encryption (DLRSIE) algorithm for encrypting the images. The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid (DNA) Strand Displacement (DNASD) approach. The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images. Then, the study presents three DNASD-related encryption rules based on the variety of DNASD, and those rules are applied for encrypting the images at the DNA sequence level. For an optimal key generation of the DLRSIE technique, the ISCA is applied with an objective function of the maximization of peak signal to noise ratio (PSNR). To examine the performance of the ISCACE-RSI model, a detailed set of simulations were conducted. The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

Improved Sine Cosine Algorithm for Optimization Problems Based on Self-Adaptive Weight and Social Strategy

Improved Sine Cosine Algorithm for Optimization Problems Based on Self-Adaptive Weight and Social Strategy

Two-Stage Optimal Active-Reactive Power Coordination for Microgrids with High Renewable Sources Penetration and Electrical Vehicles Based on Improved Sine−Cosine Algorithm

As current global trends aim at the large-scale insertion of electric vehicles as a replacement for conventional vehicles, new challenges occur in terms of the stable operation of electric distribution networks. Microgrids have become reliable solutions for integrating renewable energy sources, such as solar and wind, and are considered a suitable alternative for accommodating the growing fleet of electrical vehicles. However, efficient management of all equipment within a microgrid requires complex solving algorithms. In this article, a novel two-stage scheme is proposed for the optimal coordination of both active and reactive power flows in a microgrid, considering the high penetration of renewable energy sources, energy storage systems, and electric mobility. An improved sine-cosine algorithm is introduced to ensure the day-ahead optimal planning of the microgrid’s components aiming at minimizing the total active energy losses of the system. In this regard, both local and centralized control strategies are investigated for multiple generations and consumption scenarios. The latter proved itself a promising control scheme for the microgrid operation, as important energy loss reduction is encountered when applied.

Improved sine cosine algorithm for large-scale optimization problems

Aiming at the shortcomings of the sine cosine algorithm (SCA) in solving the large-scale optimization problems, such as low accuracy, slow convergence speed, and being easy to fall into the dimension disaster, we propose an sine cosine algorithm with Lévy flight (SCAL). By using the element-by-element multiplication of the Lévy flight distribution with the individual position vector of sine and cosine population, the characteristics and information of Lévy flight distribution are integrated into the individual information, so that it can possess the characteristic of random walk of Lévy flight and enhances the ability of local exploitation to escape from local extremum. A novel nonlinear parameter adjustment method based on spatial distance is adopted to balance the local exploitation and global exploration, which improves the convergence speed of the algorithm. On 14 classic test functions with dimensions of 100, 1 000 and 5 000 respectively, SCAL is compared with five swarm intelligence algorithms including SCA, flower pollination algorithm (FPA), particle swarm optimization (PSO) algorithm, sparrow search algorithm (SSA) and whale optimization algorithm (WOA). The experimental results indicate that SCAL has a significant advantage over the five swarm intelligence algorithms in terms of convergence accuracy, convergence speed and robustness. Compared with the improved wolf pack algorithm (IWPA), the improved flower pollination algorithm (IFPA), the improved whale optimization algorithm (IWOA), and the modified whale optimization algorithm (MWOA), which are suitable for solving large scale optimization problems, it is found that the overall optimization result of SCAL is better than the comparison algorithms and thus demonstrate that the proposed algorithm has the obvious advantages and competitiveness for solving large-scale optimization problems.