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Optimal power flow solutions for normal and critical loading scenarios using hybrid Rao-2 sine cosine algorithm

COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Mittal, Udit Nangia, Uma Jain, Narender Kumar Gupta, Saket Delhi Technol Univ Delhi 110042 India Delhi Technol Univ Dept Elect Engn Delhi 110042 India Bharati Vidyapeeths Coll Engn Dept Instrumentat & Control Engn New Delhi 110063 India

This study explores integrating a novel Hybrid Rao-2 sine cosine algorithm (HRSCA) into power systems to address optimal power flow (OPF) challenges, particularly under high loading and contingency scenarios. HRSCA combines the sine-cosine algorithm's exploratory capabilities with the Rao-2 algorithm's exploitative strengths, enhancing convergence speed and solution quality. It balances exploration and exploitation, ensuring diverse, optimal solutions that meet OPF constraints without added complexity. Rigorous testing on IEEE 30-bus and 118-bus systems demonstrates its robust performance and superiority over contemporary algorithms in standard OPF studies and scenarios like load growth and generator outages. HRSCA effectively lowers fuel costs and emissions, improves voltage stability, minimizes voltage deviations, and enhances load margin stability under operational stressors like faults. For example, in generator outage scenarios on the IEEE 30-bus system at a loading factor of 1.0932 p.u., it achieved a fuel cost of 1,021.6998 $/h, reflecting a marginal yet noteworthy 0.03 % improvement over the previously reported 1,022.0078 $/h. It also reduced power loss to 9.4336 MW, a notable 3.46 % improvement from 9.772 MW, with emission costs as low as 0.3726 ton/h and 0.3802 ton/h, respectively. For the IEEE 118-bus system, HRSCA minimized fuel costs to 129,088.63 $/h, a 1.62 % improvement over the base case of 131,220.52 $/h, outperforming many recent algorithms. These results highlight HRSCA's potential to enhance efficiency, stability, security, and environmental performance, even under critical conditions.

关键词： Rao algorithm sine cosine algorithm Security OPF Hybrid optimization Generator contingency Loadability

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Triangular-based sine cosine algorithm for global search and feature selection

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SCIENTIFIC REPORTS 2025年第1期15卷 1-43页

作者： Liu, Jiacong Bi, Chunguang Chen, Huiling Heidari, Ali Asghar Chen, He Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran

The sine cosine algorithm (SCA) is a popular population-based optimization technique that utilizes sine and cosine functions to navigate complex search spaces. However, its inherent simplicity can hinder optimal exploration-exploitation balance, particularly in high-dimensional problems, leading to slow convergence and reduced precision. To address these issues, we propose a novel triangular optimization (TO) strategy combined with a theft mechanism (TM), resulting in an enhanced algorithm named TTOSCA. We rigorously evaluate TTOSCA against 27 competing algorithms using the IEEE CEC2017 benchmark functions and apply the Wilcoxon signed-rank test to assess performance. Our results indicate that TTOSCA significantly improves precision and convergence speed. Furthermore, we develop a binary variant, BTTOSCA, to validate its effectiveness in discrete spaces through feature selection experiments on 17 datasets from UCI, including medical and high-dimensional gene data. Comparative analysis shows that BTTOSCA excels in both exploration and exploitation, achieving smaller feature subsets without compromising classification accuracy. This positions BTTOSCA as a powerful tool for feature selection in high-dimensional datasets.

关键词： sine cosine algorithm Swarm intelligence Feature selection Global optimization

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sine cosine algorithm Approaches for Directly Estimation of Power System Harmonics & Interharmonics Parameters

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IEEE ACCESS 2021年 9卷 73169-73181页

作者： Altintasi, Cagri Erzincan Binali Yildirim Univ Dept Elect & Elect Engn TR-24100 Erzincan Turkey

In order to maintain the power quality, parameters of harmonic and sub-inter-harmonic components in the signal must be estimated accurately with less computational complexity. In power systems, the value of fundamental frequency changes between 49.5Hz and 50.5Hz and this situation causes the frequency of other harmonic components to change. In order to make efficient amplitude and phase estimation, initially, frequencies must be estimated properly. In this study, the sine cosine algorithm is applied for the estimation of harmonics and sub-inter-harmonics parameters in the power systems. Unlike other optimization-based methods in the literature, it is assumed that the frequencies of the harmonic and sub-inter-harmonic components in the signal are not known. The proposed algorithm has been tested on synthetic signals at different noisy environment. Given the results obtained, SCA can predict the frequencies, amplitudes, and phases of harmonics and sub-inter-harmonics with less than 5% error, which is the maximum error limit specified by the standard, and proposed algorithm estimates the actual parameters with a low error rate even in noisy environments. In addition, SCA has a better convergence performance index than optimization algorithms used in studies in the literature assuming that frequency is known. Performance of the proposed algorithm is also tested on the data obtained from electrical transmission system. SCA estimates the frequencies of harmonics with almost no error, and the amplitude and phase estimation values of each harmonic are in corroboration with the results obtained by the DFT method.

关键词： Harmonic analysis Frequency estimation Estimation Power harmonic filters Phase estimation Heuristic algorithms Optimization Harmonic harmonic estimation sine cosine algorithm

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sine cosine algorithm with communication and quality enhancement: Performance design for engineering problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第4期10卷 1868-1891页

作者： Yu, Helong Zhao, Zisong Zhou, Jing Heidari, Ali Asghar Chen, Huiling Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China

In recent years, the sine cosine algorithm (SCA) has become one of the popular swarm intelligence algorithms due to its simple and convenient structure. However, the standard SCA tends to fall into the local optimum when solving complex multimodal tasks, leading to unsatisfactory results. Therefore, this study presents the SCA with communication and quality enhancement, called CCEQSCA. The proposed algorithm includes two enhancement strategies: the communication and collaboration strategy (CC) and the quality enhancement strategy (EQ). In the proposed algorithm, CC strengthens the connection of SCA populations by guiding the search agents closer to the range of optimal solutions. EQ improves the quality of candidate solutions to enhance the exploitation of the algorithm. Furthermore, EQ can explore potential candidate solutions in other scopes, thus strengthening the ability of the algorithm to prevent trapping in the local optimum. To verify the capability of CCEQSCA, 30 functions from the IEEE CEC2017 are analyzed. The proposed algorithm is compared with 5 advanced original algorithms and 10 advanced variants. The outcomes indicate that it is dominant over other comparison algorithms in global optimization tasks. The work in this paper is also utilized to tackle three typical engineering design problems with excellent optimization capabilities. It has been experimentally demonstrated that CCEQSCA works as an effective tool to tackle real issues with constraints and complex search space. Graphical Abstract

关键词： sine cosine algorithm communication and collaboration quality enhancement engineering design

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sine cosine algorithm-based feature selection for improved machine learning models in polycystic ovary syndrome diagnosis

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第30期83卷 75007页

作者： Rajput, Ishwari Singh Tyagi, Sonam Gupta, Aditya Jain, Vibha Graph Era Hill Univ Dept CSE Haldwani Campus Haldwani Uttarakhand India Thapar Inst Engn & Technol Patiala Punjab India Chitkara Univ Chitkara Univ Inst Engn & Technol Chandigarh Punjab India

Polycystic Ovary Syndrome (PCOS) is a hormonal state that affects women within the reproductive age group. Early and accurate diagnosis of medical conditions is crucial for the successful implementation of medication and prevention of significant health problems. In recent times, there has been an increasing amount of evidence suggesting that machine learning (ML) methods have significant potential in facilitating the diagnosis and classification of PCOS. The objective of this research is to evaluate the viability of using several machine learning algorithms, such as Random Forests, Decision Trees, Artificial Neural Networks, and XGBoost, for the purpose of classifying PCOS using clinical and laboratory features. Furthermore, the use of the sine cosine algorithm was implemented for the purpose of selecting features in order to improve the accuracy and efficiency of the model. A sample of 500 individuals was selected for the purpose of training and assessing the models, with half of the participants (250) diagnosed with PCOS and the other half not exhibiting the disease. The findings suggest that the classification algorithms had strong performance in classifying PCOS, with XGBoost having the best level of performance. In addition, the use of the sine cosine algorithm resulted in enhanced model precision and effectiveness via the identification of the most pertinent features for the classification objective. The results of this study emphasise the significant capabilities of machine learning methods, namely XGBoost and the sine cosine algorithm, in aiding the diagnosis and classification of PCOS in a medical setting. Additional investigation is required in order to substantiate the models and prove their clinical significance in the provision of patient care.

关键词： PCOS Prediction Machine Learning Meta-heuristics sine cosine algorithm XGBoost

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sine cosine algorithm for Solving Economic Load Dispatch Problem With Penetration of Renewables

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-21页

作者： Verma, Diwakar Soni, Jatin Kalathia, Dhruv Bhattacharjee, Kuntal Nirma Univ Inst Technol Ahmadabad Gujarat India

Economic load dispatch is used to allocate power demand economically among connected generators by considering various constraints. The thermal generating units are incorporated with renewable sources like wind and solar units to reduce pollution and dependency on fuel cost. The uncertainty of output power from wind and solar plants is considered here. The 2-m point estimation method is used to get generated power from wind and solar units. The population-based sine cosine algorithm is proposed to get the optimum solution of the presented complex ELD problem. The randomly placed search agents find an optimum solution according to their fitness values and keep path towards best solution attained by each search agent. The search agents avoid local optima in exploration stage and move towards the solution exploitation stage using sine and cosine functions. The proposed algorithm has been tested in various four test systems. The results proved that the proposed algorithm gives quite an effective, efficient, and promising solution compared to other techniques.

关键词： 2-m Point Estimation Method Economic Load Dispatch Renewable Energy Sources sine cosine algorithm Valve Point Effect

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sine cosine algorithm for underwater multilevel thresholding image segmentation

Sine cosine algorithm for underwater multilevel thresholding...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Yan, Zheping Zhang, Jinzhong Tang, Jialing Harbin Engn Univ Coll Automat Harbin Peoples R China

ISBN: (纸本)9781728154466

As an important and effective method, the multilevel thresholding not only attractes the attention of many scholars in recent years, but also is widely used to solve the image segmentation problem. However, the computational complexity becomes large when the threshold levels increase. To overcome this shortcoming, the sine cosine algorithm (SCA) based on Kapur's entropy method is proposed to solve the underwater multilevel thresholding image segmentation problem, which can balance exploration and exploitation to obtain the global optimal solution. To verify the effectiveness and feasibility of the SCA, the segmentation results are compared with other algorithms including bat algorithm (BA), flower pollination algorithm (FPA), particle swarm optimization (PSO), whale optimization algorithm (WOA) by maximizing fitness value of Kapur's entropy method. The fitness value, execution time, peak signal to noise ratio (PSNR), structure similarity index (SSIM) and Wilcoxon's rank-sum test are important evaluation indicators. The experimental results indicate that the SCA has a shorter execution time, higher segmentation accuracy and stronger robustness.

关键词： multilevel thresholding image segmentation sine cosine algorithm Kapur's entropy

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sine cosine algorithm Based Reduction of Higher Order Continuous Systems

Sine Cosine Algorithm Based Reduction of Higher Order Contin...

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International Conference on Intelligent Sustainable Systems (ICISS)

作者： Singh, V. P. Natl Inst Technol Dept Elect Engn Raipur Madhya Pradesh India

ISBN: (纸本)9781538619599

A novel approximation technique for reduction of higher order continuous systems utilizing sine cosine algorithm (SCA) is proposed. The parameters of reduced-order model (ROM) of the higher order continuous system (HOCS) are obtained by minimizing the normalized errors in between the Markov parameters and time moments of ROM and HOCS. The main advantage of the proposed technique is that it always engenders stable ROM for stable HOCS. The stability of ROM is ascertained utilizing Hurwitz criterion. The SCA is able to explore different regions of search space, evade local optima. This exploits promising regions of a search space during optimization efficaciously. The efficacy of the proposed technique is illustrated with the avail of one test system. A comparative study is additionally accomplished for results obtained utilizing SCA and other optimization techniques. The analysis denotes that SCA outperforms when compared to other algorithms.

关键词： model reduction optimization Pack method Routh approximation sine cosine algorithm

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

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APPLIED INTELLIGENCE 2023年第15期53卷 18581-18604页

作者： Li, Qijun Ning, Huifeng Gong, Jun Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou 730050 Gansu Peoples R China Tianshui Nomal Univ Sch Electromech & Automobile Engn Tianshui 741000 Gansu Peoples R China

As one of recently proposed optimization algorithm, sine cosine algorithm (SCA) suffers from the problems of skipping over true solutions, stagnating in local optima and premature convergence. Therefore, an improved sine cosine algorithm with heterogeneous subpopulations (HSISCA) is developed in this paper. The population of HSISCA is divided into two subpopulations which comply with different search mechanisms and information sharing rules. One subpopulation is specified to enhance exploration by incorporating Levy flight and random search guidance into SCA, while the other is specified to enhance exploitation by adaptive differential mutation operator. The exploration subpopulation is denied access to the information of exploitation subpopulation, but the exploitation subpopulation is allowed access to the information of exploration subpopulation. Moreover, the greedy selection is applied to each individual to preserve useful information. HSISCA is tested on the CEC2014 and CEC2017 benchmark functions, and is used for designing fractional order PID (FOPID) controller. The results confirm the better performance of HSISCA compared to other competitive algorithms, and demonstrate the effectiveness of HSISCA in designing FOPID controller for complex systems.

关键词： Optimization sine cosine algorithm Subpopulation Differential mutation Controller design

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

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ENERGY 2021年 221卷 119887-119887页

作者： Peng, Tian Zhang, Chu Zhou, Jianzhong Nazir, Muhammad Shahzad Huaiyin Inst Technol Coll Automat Huaian 223003 Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

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 shortterm 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. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Solar radiation forecasting Deep learning BiLSTM sine cosine algorithm CEEMDAN

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