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sine cosine algorithm with peer learning for global numerical optimization

ENGINEERING OPTIMIZATION 2025年第4期57卷 963-980页

作者： Cheng, Jiatang Lin, Qiuhong Xiong, Yan Guilin Univ Technol Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin Peoples R China

The sine cosine algorithm (SCA) is a simple and effective optimization method. However, it may exhibit stagnation behaviour in addressing complex optimization problems. Therefore, a novel improved version called the sine cosine algorithm with peer learning (PLSCA) is proposed in this study. According to the roulette wheel selection method, a peer learning strategy is designed to achieve information exchange between individuals. Then, a control parameter is introduced to control the combination of the peer learning strategy and original position updating equations, and this parameter can be adaptively adjusted by means of the individual evolution. To estimate the search capability of PLSCA, it is compared with six representative SCA methods and several other metaheuristic algorithms on two different kinds of benchmark function. Two chaotic time series problems are used to further evaluate the effectiveness of PLSCA. The results of simulation experiments reveal that PLSCA is a promising method.

关键词： sine cosine algorithm peer learning control parameter numerical optimization

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

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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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MTV-SCA: multi-trial vector-based sine cosine algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 13471-13515页

作者： Nadimi-Shahraki, Mohammad H. Taghian, Shokooh Javaheri, Danial Sadiq, Ali Safaa Khodadadi, Nima Mirjalili, Seyedali Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran Torrens Univ Ctr Artificial Intelligence Res & Optimisat Brisbane 4006 Australia Korea Univ Dept Comp Sci & Engn Seoul 02841 South Korea Nottingham Trent Univ Dept Comp Sci Clifton Lane Nottingham NG11 8NS England Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA

The sine cosine algorithm (SCA) is a metaheuristic algorithm that employs the characteristics of sine and cosine trigonometric functions. SCA's deficiencies include a tendency to get trapped in local optima, exploration-exploitation imbalance, and poor accuracy, which limit its effectiveness in solving complex optimization problems. To address these limitations, a multi-trial vector-based sine cosine algorithm (MTV-SCA) is proposed in this study. In MTV-SCA, a sufficient number of search strategies incorporating three control parameters are adapted through a multi-trial vector (MTV) approach to achieve specific objectives during the search process. The major contribution of this study is employing four distinct search strategies, each adapted to preserve the equilibrium between exploration and exploitation and avoid premature convergence during optimization. The strategies utilize different sinusoidal and cosinusoidal parameters to improve the algorithm's performance. The effectiveness of MTV-SCA was evaluated using benchmark functions of CEC 2018 and compared to state-of-the-art, well-established, CEC 2017 winner algorithms and recent optimization algorithms. The results demonstrate that the MTV-SCA outperforms the traditional SCA and other optimization algorithms in terms of convergence speed, accuracy, and the capability to avoid premature convergence. Moreover, the Friedman and Wilcoxon signed-rank tests were employed to statistically analyze the experimental results, validating that the MTV-SCA significantly surpasses other comparative algorithms. The real-world applicability of this algorithm is also demonstrated by optimizing six non-convex constrained optimization problems in engineering design. The experimental results indicate that MTV-SCA can effectively handle complex optimization challenges.

关键词： Engineering optimization problems Metaheuristic algorithms Numerical optimization Optimization algorithms sine cosine algorithm

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A Novel Quasi-Oppositional Learning-Based Chaos-Assisted sine cosine algorithm for Hybrid Energy Integrated Dynamic Economic Emission

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IETE JOURNAL OF RESEARCH 2024年第3期70卷 2453-2480页

作者： Dasgupta, Koustav Kumar Roy, Provas Mukherjee, Vivekananda Techno India Univ EE Dept Kolkata 700091 India Kalyani Govt Engg Coll EE Dept Kalyani 741235 India IIT ISM Dhanbad EEDepartment Dhanbad 826004 India

In the current era of industrialization, renewable energy (such as wind and solar energy) plays an effective role in power generation sector, and it helps to decrease the generation cost and the environmental pollution. In this work, hybrid source of energy (like solar and wind energy) has been integrated with the traditional dynamic economic emission dispatch (EED) (DEED) problem to formulate a new model, i.e., complicated constrained hybrid energy integrated DEED problem, which may be able to generate less polluted power than the traditional DEED problem. Here, the quasi-opposition learning (QOL) approach and chaotic dynamics have been introduced in a novel sine cosine algorithm (SCA) to enhance its convergence and diversity. Effectiveness of the presented solution of the dynamic thermal-wind-solar EED problem has been properly validated by solving three aspects, i.e., the cost, the emission and the combined cost-emission using QOL-based chaotic SCA. The use of solar and wind energy in the DEED problem reduces the generating costs by 8% and 20.84%, respectively, compared to traditional and wind-integrated DEED. The robustness of the proposed modified SCA has been presented by comparing the results with the results offered by SCA and other recently published algorithms.

关键词： Chaos dynamic economic emission load dispatch hybrid energy quasi-oppositional learning strategy sine cosine algorithm

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SCGJO: A hybrid golden jackal optimization with a sine cosine algorithm for tackling multilevel thresholding image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第3期83卷 7681-7719页

作者： Zhang, Jinzhong Zhang, Gang Kong, Min Zhang, Tan West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

Multilevel thresholding is a fundamental, substantial and constructive technique that has been widely recognized and concerned in recent years. However, the computational complexity rises as the threshold level raises. The golden jackal optimization (GJO) imitates discovering prey, tracking and encircling prey, and trapping prey by employing a collaborative foraging mechanism. To eliminate the GJO's drawbacks, such as premature convergence, inferior computation accuracy and sluggish convergence rate, this paper proposes a hybrid golden jackal optimization with a sine cosine algorithm (SCGJO) based on Kapur's entropy to tackle the multilevel thresholding image segmentation, the intention is to actualize the accurate threshold values and the maximal fitness values. The SCGJO not only has fantastic adaptability and reliability to promote the complementary benefits and boost the convergence accuracy but also integrates exploration and exploitation to mitigate search stagnation and arrive at the ideal value. The experimental results demonstrate that the SCGJO is superior to the other algorithms and has a quicker convergence rate, higher computation accuracy, greater segmentation quality and stronger stability. In addition, the SCGJO is a steady and trustworthy approach for tackling image segmentation.

关键词： Multilevel thresholding Golden jackal optimization sine cosine algorithm Kapur's entropy Image segmentation

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Efficient FPGA Implementation of sine cosine algorithm using High Level Synthesis

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JOURNAL OF INTERNET TECHNOLOGY 2024年第6期25卷 865-876页

作者： Pan, Jeng-Shyang Zhang, Si-Qi Chu, Shu-Chuan Hu, Chia-Cheng Wu, Jie Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Yango Univ Coll Artificial Intelligence Fuzhou Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou Peoples R China Chaoyang Univ Technol Dept Informat Management Chaoyang Taiwan

sine cosine algorithm (SCA) finds the best solution to the optimization problem by the periodicity of sine and cosine trigonometric functions. However, it is computationally intensive and contains many parameters to be determined. Fortunately, there are FPGA platforms that can be used to overcome these limitations by improving latency. sine and cosine calculation in library functions is very complex and time-consuming. Therefore, this paper proposes a hardware- accelerated CORDIC algorithm to improve the sine cosine trigonometric function that needs to be computed in the SCA algorithm. The proposed algorithm (HSCA) combines the accelerated SCA algorithm and the CORDIC algorithm. HSCA performance is tested by using six test functions run on the FPGA. The experimental results show that HSCA is 3.25 times faster and 33% fewer resource utilizations for solving optimization problems, and runs significantly faster on FPGAs with IP cores than on Soc chips in FPGAs. The performance of the HSCA algorithm is demonstrated by applying it to the TDOA localization problem.

关键词： sine cosine algorithm Filed Programmable Gate Arrays Vivado HLS tool Optimization techniques TDOA

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ASCAEO: accelerated sine cosine algorithm hybridized with equilibrium optimizer with application in image segmentation using multilevel thresholding

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EVOLVING SYSTEMS 2024年第4期15卷 1297-1358页

作者： Thapliyal, Shivankur Kumar, Narender Doon Univ Dept Comp Sci Dehra Dun Uttarakhand India

The current trend in global optimization research is to hybridize multiple variants to improve the quality of solutions to real-world problems that have recently arisen in practise. This paper introduces the accelerated sine cosine algorithm (A-SCA), which uses sine and cosine acceleration coefficients to improve SCA performance and afterwards the, accelerated SCA is combined with an Equilibrium optimizer (EO) called ASCAEO. Each nature-inspired optimization algorithm's performance relies on the operators employed for exploration and exploitation. Lacking this combination causes premature convergence, local optima trapping, and inability to attain global optima. In hybrid ASCAEO, the combination of an accelerated sine-cosine algorithm is utilized to enhance the exploration rate of classical SCA, whereas an equilibrium optimizer is used for the exploitation phase in an uncertain environment. The performance of ASCAEO is benchmarked in three test phases. Firstly, the proposed ASCAEO is validated using 38 benchmark functions with qualitative, quantitative, and scalability analyses, and its statistical findings are compared to 11 others existing metaheuristic algorithms. Secondly, ASCAEO is tested in optimizing three constrained engineering design problems for further analysis. Finally, Otsu's interclass variance function is used with the suggested ASCAEO to find the best threshold values for image segmentation. The mean inter-class variance and peak signal-to-noise ratio (PSNR) of the proposed algorithm are measured and compared to existing metaheuristics algorithms to show ASCAEO's superiority. Numerical and statistical experiments show that the hybrid variant solves benchmark and real-world applications with confined and unknown search spaces effectively.

关键词： Metaheuristics sine cosine algorithm Accelerated coefficient Equilibrium optimizer Nature inspired optimization Image segmentation

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Enhancing sine cosine algorithm based on social learning and elite opposition-based learning

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COMPUTING 2024年第5期106卷 1475-1517页

作者： Chen, Lei Ma, Linyun Li, Lvjie Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

In recent years, sine cosine algorithm (SCA) is a kind of meta-heuristic optimization algorithm with simple structure, simple parameters and trigonometric function principle. It has been proved that it has good competitiveness among the existing optimization algorithms. However, the single mechanism of SCA leads to its insufficient utilization of the information of the whole population, insufficient ability to jump out of local optima and poor performance at solving complex objective function. Therefore, this paper introduces social learning strategy (SL) and elite opposition-based learning (EOBL) strategy to improve SCA, and proposes novel algorithm: enhancing sine cosine algorithm based on elite opposition-based learning and social learning (ESLSCA). Social learning strategy takes full advantage of information from the entire population. The elite opposition-based learning strategy provides a possibility for the algorithm to jump out of local optima and increases the diversity of the population. To demonstrate the performance of ESLSCA, this paper uses 22 well-known benchmark test functions and CEC2019 test function set to evaluate ESLSCA. The comparisons show that the proposed ESLSCA has better performance than the standard SCA and it is very competitive among other excellent optimization algorithms.

关键词： Global optimization sine cosine algorithm Meta-heuristic algorithm Social learning Elite opposition-based learning

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Handling the Class Imbalance Problem With an Improved sine cosine algorithm for Optimal Instance Selection

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IEEE ACCESS 2024年 12卷 87131-87151页

作者： Moorthy, Rajalakshmi Shenbaga Selvaraj, Arikumar K. Prathiba, Sahaya Beni Yenduri, Gokul Mohanty, Sachi Nandan Ramesh, Janjhyam Venkata Naga Sri Ramachandra Inst Higher Educ & Res Fac Engn & Technol Chennai 600116 India SRM Inst Sci Technol SRMIST Coll Engn & Technol Dept Data Sci & Business Syst Kattankulathur 603203 India Vellore Inst Technol Ctr Cyber Phys Syst Sch Comp Sci & Engn Chennai 600127 India VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati 522237 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada 522302 Andhra Pradesh India

Class imbalance is a significant study problem that is biased, exhibiting excellent performance toward the majority classes in the dataset while showing inferior performance toward minority classes. When dealing with real-world issues, this kind of biased nature affects classification accuracy. The Improved Binary sine cosine algorithm (IBSCA) has been used in this work to identify a subset of the majority class in the best possible way. The proposed IBSCA makes some enhancements over the conventional Binary sine cosine algorithm (BSCA) to address the issue of premature convergence with local optimal solutions. To improve classification accuracy for unbalanced datasets, the proposed IBSCA seeks to identify the optimal collection of instances from the majority class. The advised IBSCA makes use of the alpha agent, beta agent, and random agent's location, which tends to devote considerable time to exploration to find the best possible set of instances. By using the geometric mean (G-mean) and F-score to describe the fitness function, the proposed IBSCA aims to solve the multi-objective optimization issue. On 18 datasets with different imbalance ratios taken from the KEEL repository, experimentation is conducted. Comparisons are made between the suggested IBSCA and the traditional Binary sine cosine algorithm, Binary Particle Swarm Optimization (BPSO), and Binary Grey Wolf Optimization (BGWO). Additionally, the performance of the suggested IBSCA is evaluated against the top outcomes from different research papers. Metrics like sensitivity, F-score, G-mean, and area under curve (AUC) show that the suggested IBSCA outperforms the state-of-the-art algorithms. The statistical findings using the Wilcoxon signed rank test and Friedman test also demonstrate that the suggested IBSCA is more efficient than the other conventional algorithms.

关键词： Classification algorithms Metaheuristics Convergence Power capacitors Clustering algorithms Machine learning algorithms Heuristic algorithms Class imbalance instance selection K-nearest neighbor metaheuristic algorithm sine cosine algorithm

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