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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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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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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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Nonlinear System Identification Using Adaptive Volterra Model Optimized with sine cosine algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第11期47卷 14411-14422页

作者： Singh, Sandeep Rawat, Tarun Kumar Ashok, Alaknanda Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India Netaji Subhas Univ Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttarakhand India

This paper presents the handling of nonlinear system identification problem based on Volterra model. Gradient-based algorithms are generally applied to solve system identification problems. However, these algorithms have the limitation of getting trapped in the local minimum. In the presented work, a novel population-based optimization algorithm popularly known as sine cosine algorithm (SCA) is being utilized for the identification of nonlinear discrete-time system. The SCA uses mathematical sine and cosine functions for the purpose of optimization. SCA is responsible for the creation of multiple random solutions and moving them towards best solution while maintaining proper balance between the exploitation and exploration phases of optimization. The performance evaluation of the applied SCA is carried out in terms of coefficient evaluation, mean square error and convergence profile. Two different examples for nonlinear system are presented in this work so as to demonstrate the validity of the employed algorithm. Performance analysis of the proposed approach with the existing state-of-the-art algorithms proves that the SCA outperforms the other algorithms.

关键词： Nonlinear system Volterra model sine cosine algorithm Gravitational search algorithm Particle swarm optimization algorithm Cuckoo search algorithm

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Demand Response Based Optimal Microgrid Scheduling Problem Using A Multi-swarm sine cosine algorithm

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第8期18卷 2157-2177页

作者： Qiu, Chenye Fang, Huixing Huangshan Univ Sch Informat Engn Huangshan 245000 Peoples R China

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.

关键词： Demand response microgrid scheduling sine cosine algorithm multi-swarm approach

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Optimization of reservoir operation by sine cosine algorithm: A case of study in Algeria

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 44卷

作者： Boudjerda, Merouane Touaibia, Benina Mihoubi, Mustapha Kamel Kisi, Ozgur Ehteram, Mohammd El-Shafie, Ahmed MVRE Ecole Natl Super Hydraul Blida Algeria Ilia State Univ Sch Technol Tbilisi Georgia Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Fac Engn Civil Engn Dept Kuala Lumpur Malaysia Univ Malaya Kuala Lumpur Malaysia

The optimal operation of the reservoir has vital importance in water engineering. In the presented article, a new optimization method, named sine cosine algorithm (SCA) was employed to obtain operating policy for an irrigation system. The SCA was utilized for the monthly operation of the Boukerdane Dam placed in the north of Algeria. The fitness function was the minimization of the total shortage for the studied period. Three scenarios considering three different seasons of inflow (dry, normal and wet) are used to optimize the reservoir system's operation. The SCA outputs were compared with particle swarm optimization (PSO) and kidney algorithm (KA). The outcomes indicated that the SCA surpassed the PSO and KA in convergence rate. The general results indicated the low speed of KA and PSO in achieving convergence. The results indicated that the highest RES (resiliency index), SUS (sustainability index) and REL (reliability index) achieved by the SCA were 65, 86 and 92 %, respectively. Comparing the third scenario with the first and second scenarios, it was observed that the third scenario (wet seasons) improved the results.

关键词： Water resources management Reservoir operation sine cosine algorithm Kidney algorithm Particle swarm optimization Sustainability index

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Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 253卷

作者： Nasser, Abdullah B. Ghanem, Waheed Ali H. M. Saad, Abdul-Malik H. Y. Abdul-Qawy, Antar Shaddad Hamed Ghaleb, Sanaa A. A. Alduais, Nayef Abdulwahab Mohammed Din, Fakhrud Ghetas, Mohamed Univ Vaasa Sch Technol & Innovat Vaasa Finland Univ Malaysia Terengganu Fac Comp Sci & Math Kuala Terengganu Malaysia Univ Buraimi Coll Engn Buraimi Oman Univ Sultan Zainal Abidin Fac Informat & Comp Kuala Terengganu Terengganu Malaysia Hodeidah Univ Fac Comp Sci & Engn Al Hudaydah Yemen Univ Aden Fac Engn Aden Yemen Abdulrahman Al Sumait Univ Fac Sci Zanzibar Tanzania Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Parit Raja Batu Pahat Johor Malaysia Univ Malakand Dept Comp Sci & IT Totakan Pakistan Galala Univ Dept Comp Sci Suez Egypt

Software Defect Prediction (SDP) plays a vital role in the software development life cycle as it helps identify and fix software defects. However, predicting software defects with irrelevant features and overlapping classes is challenging and can lead to lengthy training and low model accuracy. To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive sine cosine algorithm, named Depth Adaptive sine cosine Feature Selection (DASC-FS). DASC-FS integrates the Adaptive sine cosine algorithm (ASCA) as a search algorithm to determine the relevant features and adopts Depth Linear Discriminant Analysis (D-LDA) to identify the discriminative features that maximize class separation. The paper proposes ASCA which is a metaheuristic algorithm meticulously designed to enhance the search capabilities of the standard sine cosine algorithm (SCA). Combining the simplicity of the SCA with the efficiency of multiple mutation operators inspired by Genetic algorithms (GA), ASCA enhances the diversity of the solutions and imparts remarkable adaptability to various situations. Furthermore, this study introduces a novel linear discriminant method, called Depth Linear Discriminant Analysis (D-LDA) to enhance the robustness of the original LDA. D-LDA systematically integrates the matrix depth concept into LDA, offering a systematic approach to address the challenges associated with scatter matrix estimation. As matrix depth measures how central or deep a particular matrix is within a distribution with respect to different directions, it is an efficient tool for computing a robust scatter matrix estimator that can handle outliers and complex data structures. The experimental results showed that DASC-FS consistently obtains the highest accuracy compared to most existing methods by integrating ASCA and D-LDA, thereby considering both accuracy optimization and class separation. The results also show that the use

关键词： Software defect prediction Machine learning Feature selection Metaheuristic algorithms sine cosine algorithm Linear discriminant analysis

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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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