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A novel feature selection method for data mining tasks using hybrid sine cosine algorithm and Genetic algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第3期24卷 2161-2176页

作者： Abualigah, Laith Dulaimi, Akram Jamal Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan

Feature selection (FS) is a real-world problem that can be solved using optimization techniques. These techniques proposed solutions to make a predictive model, which minimizes the classifier's prediction errors by selecting informative or important features by discarding redundant, noisy, and irrelevant attributes in the original dataset. A new hybrid feature selection method is proposed using the sine cosine algorithm (SCA) and Genetic algorithm (GA), called SCAGA. Typically, optimization methods have two main search strategies;exploration of the search space and exploitation to determine the optimal solution. The proposed SCAGA resulted in better performance when balancing between exploitation and exploration strategies of the search space. The proposed SCAGA has also been evaluated using the following evaluation criteria: classification accuracy, worst fitness, mean fitness, best fitness, the average number of features, and standard deviation. Moreover, the maximum accuracy of a classification and the minimal features were obtained in the results. The results were also compared with a basic sine cosine algorithm (SCA) and other related approaches published in literature such as Ant Lion Optimization and Particle Swarm Optimization. The comparison showed that the obtained results from the SCAGA method were the best overall the tested datasets from the UCI machine learning repository.

关键词： Optimization problems Feature selection sine cosine algorithm Genetic algorithm Hybridization

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Advances in sine cosine algorithm: A comprehensive survey

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第4期54卷 2567-2608页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan New York Univ Abu Dhabi Div Engn Saadiyat Isl 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

The sine cosine algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance of the SCA against similar algorithms.

关键词： sine cosine algorithm Meta-heuristic optimization algorithms Optimization problems Population-based algorithms

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Improved sine cosine algorithm for Optimization Problems Based on Self-Adaptive Weight and Social Strategy

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IEEE ACCESS 2023年 11卷 73053-73061页

作者： Chun, Ye Hua, Xu Jiangsu Vocat Coll Informat Technol Internet Things Engn Coll Wuxi 214001 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214001 Peoples R China

The sine cosine algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy sine cosine algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.

关键词： sine cosine algorithm self-adaptive weight social strategy complex large-scale problems

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Economic load dispatch using memetic sine cosine algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 11685-11713页

作者： Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu Zitar, Raed Assaleh, Khaled Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Sorbonne Univ Abu Dhabi Sorbonne Univ Ctr Artificial Intelligence Abu Dhabi U Arab Emirates

In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. As other optimization methods, SCA has main shortcoming in exploitation process when a non-linear constraints problem like ELD is tackled. Therefore, beta-hill climbing optimizer, a recent local search algorithm, is hybridized as a new operator in SCA to empower its exploitation capability to tackle ELD. The proposed hybrid algorithm is abbreviated as SCA-beta HC which is evaluated using two sets of real-world generation cases: (i) 3-units, two versions of 13-units, and 40-units, with neglected Ramp Rate Limits and Prohibited Operating Zones constraints. (ii) 6-units and 15-units with Ramp Rate Limits and Prohibited Operating Zones constraints. The sensitivity analysis of the control parameters for SCA-beta HC is initially studied. The results show that the performance of the SCA-beta HC algorithm is increased by tuning its parameters in proper value. The comparative evaluation against several state-of-the-art methods show that the proposed method is able to produce new best results for some tested cases as well as the second-best for others. In a nutshell, hybridizing beta HC optimizer as a new operator for SCA is very powerful algorithm for tackling ELD problems.

关键词： Economic load dispatch Hybrid metaheuristics sine cosine algorithm beta-hill climbing optimizer Power systems

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Image registration of computed tomography of lung infected with COVID-19 using an improved sine cosine algorithm

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第9期60卷 2521-2535页

作者： Dida, Hedifa Charif, Fella Benchabane, Abderrazak Univ Kasdi Merbah Dept Elect & Telecommun Lab Genie Elect LAGE Ouargla Algeria Univ Kasdi Merbah Dept Elect & Telecommun Ouargla Algeria

In recent years, the optimization problem using meta-heuristic algorithms has been widely used in medical image registration and was a solution in diagnosing many diseases and tumors. Given the great success achieved by the sine cosine algorithm (SCA) and particle swarm optimization (PSO) algorithms in many medical images analysis, and the use of the computed tomography (CT) scan images for diagnosing COVID-19 patients, we propose an improved sine cosine algorithm (ISCA) resulting from the hybridization of the SCA and PSO algorithms to register the CT images of the lung of the people infected by COVID-19. Simulation results show that the proposed approach can achieve high accuracy and robust recording compared to the SCA method.

关键词： COVID-19 sine cosine algorithm Particle swarm optimization Image registration

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An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 2633-2662页

作者： Moghdani, Reza Elaziz, Mohamed Abd Mohammadi, Davood Neggaz, Nabil Persian Gulf Univ Ind Management Dept Boushehr Iran Zagazig Univ Fac Sci Dept Math Zagazig Egypt Payam Noor Univ Ind Engn Dept Asalouyeh Iran USTO MB BP 1505 El Mnaouer 31000 Oran Algeria Fac Math & Informat Dept Informat Lab SIMPA Oran Algeria

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

关键词： Metaheuristic Global optimization Volleyball premier league sine cosine algorithm

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An application of sine cosine algorithm-based fuzzy possibilistic c-ordered means algorithm to cluster analysis

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SOFT COMPUTING 2021年第5期25卷 3469-3484页

作者： Kuo, R. J. Lin, Jun-Yu Thi Phuong Quyen Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Danang Univ Sci & Technol Fac Project Management 54 Nguyen Luong Bang Danang Vietnam Powertech Technol Inc 12Aly 13Ln 211Linsen E Rd Chiayi 600 Taiwan

Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognition, social science, and so on. However, in real-world applications, there are a lot of noises or outliers which will seriously influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. To overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means and fuzzy c-ordered means to propose a fuzzy possibilistic c-ordered means (FPCOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (SCA) combined with FPCOM to improve the clustering results. The proposed algorithm is named SCA-FPCOM algorithm. Ten benchmark datasets collected from the UCI machine repository were used to validate the proposed algorithm in terms of adjusted rand index and the Silhouette coefficient. According to the experimental results, the SCA-FPCOM algorithm can obtain better results than other algorithms.

关键词： Clustering analysis sine cosine algorithm Outliers Fuzzy c-means algorithm Possibilistic fuzzy c-means algorithm Fuzzy c-ordered means algorithm

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Context based image segmentation using antlion optimization and sine cosine algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第19期77卷 25761-25797页

作者： Oliva, Diego Hinojosa, Salvador Abd Elaziz, Mohamed Ortega-Sanchez, Noe Univ Guadalajara CUCEI Div Elect & Comp Ave Revoluc 1500 Guadalajara Jal Mexico Univ Complutense Madrid Fac Informat Dept Ingn Software & Inteligencia Artificial E-28040 Madrid Spain Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Zagazig Univ Dept Math Fac Sci Zagazig Egypt

Multilevel thresholding (MTH) is one of the most commonly used approaches to perform segmentation on images. However, as most methods are based on the histogram of the image to be segmented, MTH methods only consider the occurrence frequency of certain intensity level disregarding all spatial information. Contextual information can help to enhance the quality of the segmented image as it considers not only the value of the pixel but also its vicinity. The energy curve was designed to bring spatial information into a curve with the same properties as the histogram. In this paper, two recently proposed Evolutionary Computational algorithms (ECAs) are coupled with two classical thresholding criteria to perform MTH over the energy curve. The selected ECAs are the Antlion Optimizer (ALO) and the sine cosine algorithm (SCA). The proposed methods are evaluated intensively regarding quality, and a statistical analysis is presented to compare the results of the algorithms against similar approaches. Experimental evidence encourages the use ALO for MTH while it concludes that SCA does not outperform other ECAs form the state-of-the-art.

关键词： Antlion Optimization sine cosine algorithm Multilevel Thresholding Energy Curve

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Compact sine cosine algorithm applied in vehicle routing problem with time window

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TELECOMMUNICATION SYSTEMS 2021年第4期78卷 609-628页

作者： Pan, Jeng-Shyang Yang, Qing-yong Chu, Shu-Chuan Chang, Kuo-Chi Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China

In this paper, the compact sine cosine algorithm (cSCA) is proposed. The cSCA algorithm is not based on population, but simulates the behavior of the actual population through a probability model called virtual population. Compared with the original algorithm, the cSCA algorithm takes up less memory space. However, frequent sampling may lead to poor solution quality. In view of this situation, this paper introduces the intergenerational generation sampling mechanism to improve the cSCA algorithm. Through the CEC2013 function set test, compared with the original SCA algorithm and other compact algorithms, the algorithm proposed in this paper can show strong solving ability. Finally, this paper describes how to apply the proposed algorithm and the SCA algorithm to solve the vehicle routing problem with time window in transportation. The quality of the solution is further improved by introducing the relocate operator. Through Solomon standard test data, the calculation performance of the algorithms is verified.

关键词： sine cosine algorithm Compact optimization VRPTW Intergeneration sampling

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Optimal design of power system stabilizer using sine cosine algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2019年第3期34卷 1330-1350页

作者： Ekinci, Serdar Batman Univ Muhendislik & Mimarlik Fak Bilgisayar Muhendisligi Bolumu TR-72060 Batman Turkey

In this study, sine cosine algorithm (SCA), as a new meta-heuristic method, is proposed for optimal design of power system stabilizer (PSS). For the PSS design problem, a new multi-objective function consisting of eigenvalue and time-domain based objective functions is considered and with the SCA technique, this proposed objective function was minimized and the optimal parameters of the stabilizer were found. The robustness and effectiveness of the stabilizer designed with SCA was tested to suppress the low-frequency oscillations in power systems, which are exposed to different disturbances. The proposed SCA-based PSS (SCAPSS) results were compared with bat algorithm based PSS (BAPSS) and symbiotic organisms search algorithm based PSS (SOSPSS). The superiority of the designed SCAPSS was verified by linear model stability analysis and nonlinear time-domain simulation. The results of the analysis have showed that the proposed novel approach provides faster damping and minimal overshoot of the low frequency oscillations in the rotor of the synchronous machine. In addition, SCA has performed better than the other two algorithms in terms of accuracy of final solution, convergence speed, computation time, damping ratio, and settling time of rotor angle.

关键词： Power system stabilizer dynamic stability sine cosine algorithm bat algorithm symbiotic organisms search algorithm

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