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Improved sine cosine algorithm with simulated annealing and singer chaotic map for Hadith classification

NEURAL COMPUTING & APPLICATIONS 2022年第2期34卷 1385-1406页

作者： Tubishat, Mohammad Ja'afar, Salinah Idris, Norisma Al-Betar, Mohammed Azmi Alswaitti, Mohammed Jarrah, Hazim Ismail, Maizatul Akmar Omar, Mardian Shah Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Univ Malaya Dept Linguist Acad Malay Studies Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Ajman Univ Artificial Intelligence Res Ctr AIRC Dept Informat Technol MSAI Coll Engn & Informat Technol Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Xiamen Univ Malaysia Sch Elect & Comp Engn ICT Sepang 43900 Selangor Darul Malaysia Skyline Univ Coll Sch Informat Technol Sharjah U Arab Emirates

Feature selection (FS) represents an important task in classification. Hadith represents an example in which we can apply FS on it. Hadiths are the second major source of Islam after the Quran. Thousands of Hadiths are available in Islam, and these Hadiths are grouped into a number of classes. In the literature, there are many studies conducted for Hadiths classification. sine cosine algorithm (SCA) is a new metaheuristic optimization algorithm. SCA algorithm is mainly based on exploring the search space using sine and cosine mathematical formulas to find the optimal solution. However, SCA, like other Optimization algorithm (OA), suffers from the problem of local optima and solution diversity. In this paper, to overcome SCA problems and use it for the FS problem, two major improvements were introduced to the standard SCA algorithm. The first improvement includes the use of singer chaotic map within SCA to improve solutions diversity. The second improvement includes the use of the Simulated Annealing (SA) algorithm as a local search operator within SCA to improve its exploitation. In addition, the Gini Index (GI) is used to filter the resulted selected features to reduce the number of features to be explored by SCA. Furthermore, three new Hadith datasets were created. To evaluate the proposed Improved SCA (ISCA), the new three Hadiths datasets were used in our experiments. Furthermore, to confirm the generality of ISCA, we also applied it on 14 benchmark datasets from the UCI repository. The ISCA results were compared with the original SCA and the state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Genetic algorithm (GA), Grasshopper Optimization algorithm (GOA), and the most recent optimization algorithm, Harris Hawks Optimizer (HHO). The obtained results confirm the clear outperformance of ISCA in comparison with other optimization algorithms and Hadith classification baseline works. From the obtained results, it is inferred that ISCA can simulta

关键词： sine cosine algorithm Simulated annealing Hadith text classification Chaotic maps

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Transformer Fault Diagnosis Based on an Improved sine cosine algorithm and BP Neural Network

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2022年第6期15卷 502-510页

作者： Cheng, Jiatang Feng, Zhichao Xiong, Yan Guilin Univ Technol Coll Mech & Control Engn Guilin 541006 Peoples R China

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.

关键词： Transformer fault diagnosis sine cosine algorithm cloud model BP neural network simeelation

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Clustering method and sine cosine algorithm for image segmentation

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 669-682页

作者： Khrissi, Lahbib El Akkad, Nabil Satori, Hassan Satori, Khalid Sidi Mohamed Ben Abdellah Univ LISAC Dept Comp Sci Fac Sci Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Syst & Applicat ENSA Fez Fes Morocco

This article presents a new image segmentation approach based on the principle of clustering optimized by the meta-heuristic algorithm namely: SCA (algorithm Sinus cosine). This algorithm uses a mathematical model based on trigonometric functions to solve optimization problems. Such an approach was developed to solve the drawbacks existing in classic clustering techniques such as the initialization of cluster centers and convergence towards the local optimum. In fact, to obtain an "optimal" cluster center and to improve the image segmentation quality, we propose this technique which begins with the generation of a random population. Then, we determine the number of clusters to exploit. Later, we formulate an objective function to maximize the interclass distance and minimize the intra-class distance. The resolution of this function gives the best overall solution used to update the rest of the population. The performances of the proposed approach are evaluated using a set of reference images and compared to several classic clustering methods, like k-means or fuzzy c-means and other meta-heuristic approaches, such as genetic algorithms and particle swarm optimization. The results obtained from the different methods are analyzed based on the best fitness values, PSNR, RMSE, SC, XB, PC, S, SC, CE and the computation time. The experimental results show that the proposed approach gives satisfactory results compared to the other methods.

关键词： Clustering Classification Image segmentation sine cosine algorithm Metaheuristic

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Cloud model based sine cosine algorithm for solving optimization problems

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EVOLUTIONARY INTELLIGENCE 2019年第4期12卷 503-514页

作者： Cheng, Jiatang Duan, Zhimei Honghe Univ Engn Coll Mengzi 661199 Peoples R China

sine cosine algorithm (SCA) is a recently developed optimization technique, which uses sine function and cosine function as operators to find the global optimal solution. However, proper parameter setting is a challenging task. Only using the number of iterations to adjust the algorithm parameters cannot fully reflect the convergence information in the evolution process, so SCA lacks the adaptability in solving different optimization problems. To address this issue, a cloud model based sine cosine algorithm (CSCA) is proposed. In CSCA, the cloud model is used to adjust the control parameter adaptively while keeping SCA algorithm framework unchanged. The performance of the presented CSCA method is evaluated using 13 benchmark test functions with different dimensions. Experimental results demonstrate that the proposed algorithm is superior to other SCA variants in terms of robustness and scalability.

关键词： sine cosine algorithm Cloud model Parameter adjustment Optimization

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Least Cost Generation Expansion Planning considering Renewable Energy Resources Using sine cosine algorithm

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IEEE ACCESS 2022年第5期10卷 6185-6203页

作者： Abbas, Tauseef Ashraf, Muhammad Mansoor Malik, Tahir Nadeem HITEC Univ Dept Elect Engn Taxila 47050 Pakistan Univ Engn & Technol Dept Elect Engn Taxila 47050 Pakistan

Ever-growing development in technology and rising energy demand playing a vital role in seeking the attention of power planners to design such systems which fulfill the electrical demand by minimizing the generation cost through the optimum strategies. Nowadays, Generation Expansion Planning (GEP) is very important to make an adequate energy management system, and it is a highly nonlinear, constrained optimization problem and has been solved in a mathematical programming environment. However, due to the complexity of the GEP problem and the limitations of mathematical programming techniques, the meta-heuristic method has the capability to handle the GEP problem. The sine cosine algorithm (SCA) is a novel population-based algorithm that can handle complex constrained optimization problems. In this research, the GEP problem is mapped into SCA in the presence of Renewable Energy Resources (RER) subject to technical and environmental constraints. The developed SCA-PFA is tested for validation, and case studies are solved using standard test systems. The obtained results are better in terms of cost and computational time when compared with results available in the literature. This shows the promise of the approach.

关键词： Generation expansion planning sine cosine algorithm Renewable energy resources Power system planning Meta-heuristic optimization Equivalent energy function method

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Optimization of PID controller for Hybrid Renewable Energy System using Adaptive sine cosine algorithm

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2020年第2期10卷 669-677页

作者： Yousef, Ali M. Ebeed, Mohamed Abo-Elyousr, Farag K. Elnozohy, Ahmed Mohamed, Moayed Abdelwahab, Saad A. Mohamed Assiut Univ Fac Engn Dept Elect Engn Assiut Egypt Suez Univ Fac Ind Educ Dept Elect Suez Egypt Sohag Univ Fac Engn Dept Elect Engn Sohag Egypt Sohag Univ Fac Ind Educ Dept Elect Engn Sohag Egypt Minist Higher Educ High Inst Elect Engn Bilbis Sharqiya Egypt

This paper proposed a new application of an efficient Adaptive sine cosine algorithm (ASCA) to determine the optimal settings of the PID controllers in hybrid renewable energy system (HRES). The ASCA is proposed to enhance the searching capabilities of the traditional sine cosine Optimization (SCA) and its stagnation to local optima The ASCA is based on modifying traditional SCA by applying the Levy flight distribution and adaptive operators. The HRES consists of three sources photovoltaic (PV) source, wind turbine and battery storage. These sources are connected to a DC/DC boost converter for converting the DC voltage to an AC voltage through three-phase inverter. The considered objective function is formulated in terms of the current and voltage errors to enable the HRES to participate effectively within the connected micro-grid via optimal gains of the PID controllers. The results verify that the performance of the HRES is enhanced considerably by optimizing the parameters of the HRES controllers using the ASCA under several operating conditions of solar irradiation, temperature and wind speed.

关键词： PID controller Optimization Renewable Energy System Micro-grid sine cosine algorithm Levy flight distribution

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Enhanced sine cosine algorithm using opposition learning, adaptive evolution and neighborhood search strategies for multivariable parameter optimization problems

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APPLIED SOFT COMPUTING 2022年第0期119卷 108562-108562页

作者： Feng, Zhong-kai Duan, Jie-feng Niu, Wen-jing Jiang, Zhi-qiang Liu, Yi Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China

sine cosine algorithm (SCA), an emerging metaheuristic method, is usually limited by the local convergence and search stagnation defects in multivariable optimization problems. To improve the SCA performance, this study proposes an enhanced sine cosine algorithm (ESCA) using several modified strategies, including the opposition learning strategy for enlarging search range, the adaptive evolution strategy for improving global exploration, the neighborhood search strategy for increasing population diversity, and the greedy selection strategy for guaranteeing solution quality. ESCA and several meta heuristics methods are used to solve a group of numerical optimization problems. The experimental results indicate that in terms of solution efficiency and convergence rate, ESCA outperforms several traditional methods for multivariable parameter optimization problems. Then, several engineering optimization problems are employed to further test the feasibility of the ESCA method in practical applications. The simulations show that for various performance evaluation indexes, ESCA can produce high-quality solutions with better objective values compared to the control methods. Thus, a simple but powerful tool is developed to address the complex multivariable parameter optimization problems.(c) 2022 Elsevier B.V. All rights reserved.

关键词： sine cosine algorithm Numerical optimization Engineering optimization Parameter optimization Evolutionary algorithm

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Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

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TEXTILE RESEARCH JOURNAL 2020年第17-18期90卷 2007-2021页

作者： Zhou, Zhiyu Zhang, Ruoxi Zhang, Jianxin Wang, Yaming Zhu, Zefei Liu, Chengxia Zhejiang Sci Tech Univ Sch Informat Sci & Technol Xiasha Hang Zhou Zhejiang Peoples R China Zhejiang Sci Tech Univ Res Ctr Modern Text Machinery Technol Xiasha Hang Zhou Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Zhejiang Sci Tech Univ Fash Acad Xiasha Hang Zhou Zhejiang Peoples R China

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized;that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.

关键词： wrinkle level classification online sequential extreme learning machine sine cosine algorithm differential evolution

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Power flow based hydro-thermal-wind scheduling of hybrid power system using sine cosine algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 178卷 106018-000页

作者： Dasgupta, Koustav Roy, Provas Kumar Mukherjee, Vivekananda Techno India Univ Dept Elect Engn Salt Lake W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Indian Sch Mines Dept Elect Engn Indian Inst Technol Dhanbad Jharkhand India

Different types of optimization techniques are being applied in hydrothermal scheduling (HTS) problem for optimizing fuel cost, emission and combined cost emission. Usage of fossil fuel for thermal power generation increases global warming and environment pollution. In this work, environment friendly, clean energy such as wind energy, has been integrated with the HTS problem to overcome the effect of thermal pollution and to reduce generation cost. A novel sine cosine algorithm (SCA) has been implemented to minimize generation cost and fuel emission. The different control parameters of the SCA have been properly utilized to balance the exploration and the exploitation phases leading to find out near global optimal solution. To study its performance and efficiency, SCA has been applied to solve different cases (ie., economic load scheduling, economic emission scheduling, combined economic emission scheduling) of HTS and hydrothermal wind scheduling (HTWS) problems. Furthermore, optimal power flow (OPF) based HTWS is studied for a standard 9-bus system. Results offered by some newly surfaced algorithms (like teaching learning-based optimization, gravitational search algorithm, real-coded genetic algorithm etc.) have been compared with those offered by the SCA to establish its effectiveness. Utility of wind energy in scheduling problem has been proposed in this work.

关键词： Cumulative density function Short-term hydrothermal scheduling sine cosine algorithm Weibull probability density function Wind energy

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On Lifetime Enhancement of Super Nodes based Wireless Sensor Networks by using sine cosine algorithm

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2019年第5期35卷 1147-1159页

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Balas, Valentina E. Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikk Dept Elect & Elect Engn Sikkim 737139 India Aurel Vlaicu Univ Arad Fac Engn Arad 310130 Romania

Wireless Sensor Networks (WSNs) are consists of many tiny sensing devices called nodes to sense, compute and transmit the data. These nodes are made of transducer, memory, battery, micro-controller and an antenna for the communication of data from node to sinks. Nodes used in the networks are having energy constraints as they are usually battery powered and hence it is not always feasible to change the battery due to odd terrain of deployment. For the managements of these limited energy resources of WSNs various algorithms have been proposed and implemented. In this paper we have implemented the sine cosine Optimization algorithm in routing and clustering of WSNs for the optimizing lifetime of the Wireless Sensor Networks. Higher energy nodes are deployed to work as the cluster head to enhance the lifetime of WSNs and effect of using higher energy nodes are also studied for two different position of base station (BS). Results of this algorithm are compared with the Genetic algorithm and Particle Swarm Optimization algorithm.

关键词： wireless sensor networks clustering routing particle swarm optimization genetic algorithm sine cosine algorithm

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