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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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A Modified sine cosine algorithm With Teacher Supervision Learning for Global Optimization

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IEEE ACCESS 2021年 9卷 17744-17766页

作者： Xian, Hang Yang, Chenglin Wang, Houjun Yang, Xiaoyan Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

The sine cosine algorithm (SCA) is a recently developed meta-heuristic algorithm for solving global optimization problems. It has shown excellent performance in meta-heuristic algorithms. But this algorithm also has shortcomings such as low accuracy, easy to fall into a local solution, and slow convergence speed. Aiming at these deficiencies of the SCA, a modified sine cosine algorithm with teacher supervision learning (TSL-SCA) for global optimization is proposed. First, the teacher supervision strategy can guide the population convergence and accelerate the convergence speed. Second, individuals perform reflective learning after the standard SCA position is updated, which can effectively prevent individuals from stagnating in the evolutionary process and increase population diversity. In addition, a hybrid inverse learning method is proposed. It can not only enhance the ability of finding global optimal solution and increase population distributivity, but also balance the exploration and exploitation capabilities. Differential evolution algorithm (DE), particle swarm optimization (PSO), cuckoo search (CS) algorithm, moth-flame optimization (MFO), whale optimization algorithm (WOA), Teaching-Learning-Based Optimization (TLBO), SCA and TSL-SCA are selected for simulation experiment to solve 33 benchmark optimization problems. The experimental results show that the TSL-SCA can significantly enhance the optimization accuracy and convergence speed. Furthermore, the effectiveness of the proposed method is examined by solving analog circuit fault diagnosis of filter circuit examples.

关键词： Optimization Convergence Statistics Sociology Heuristic algorithms Search problems Particle swarm optimization sine cosine algorithm teacher supervision learning global optimization fault diagnosis

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Parallel and Distributed Implementation of sine cosine algorithm on Apache Spark Platform

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IEEE ACCESS 2021年 9卷 77188-77202页

作者： Alfailakawi, Mohammad Gh. Aljame, Maryam Ahmad, Imtiaz Kuwait Univ Dept Comp Engn Khaldiyah 13060 Kuwait Minist Educ Educ Software Dept Kuwait 12013 Kuwait

The sine cosine algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other meta-heuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

关键词： Optimization Sparks Cluster computing Clustering algorithms Search problems Standards Machine learning algorithms Apache spark cluster Hadoop meta-heuristics sine cosine algorithm

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A parameter adaptive sine cosine algorithm for global optimization problems

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷 015253-015253页

作者： Cheng, Jiatang Song, Peisen Xiong, Yan Guilin Univ Technol Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin 541006 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin 541006 Peoples R China

sine cosine algorithm (SCA) is a random search technique for global numerical optimization. However, SCA still exhibits low efficiency for some complex problems. In this paper, we develop a novel SCA variant (PASCA) by introducing a parameter adaptive mechanism. Specifically, the control parameter is produced based on a Cauchy distribution rather than the original linear decreasing scheme. After that, at the end of each generation, the successful historical information is utilized to dynamically update the control parameter, which helps to strengthen the search ability and reduce dependence on the problem to be solved. To verify the effectiveness of PASCA algorithm, comparison experiments are conducted on 43 benchmark functions including 13 classic problems and CEC 2017 test suite as well as a time series prediction problem. Simulation results demonstrate that PASCA is an efficient and promising optimization method compared with the classic SCA and its four recent strong variants.

关键词： sine cosine algorithm control parameter adaptive mechanism optimization

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A Novel Hybrid Harris Hawk Optimization-sine cosine algorithm for Congestion Control in Power Transmission Network

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ENERGIES 2024年第19期17卷 4985-4985页

作者： Kumar, Vivek Rao, R. Narendra Ansari, Md Fahim Shekher, Vineet Paul, Kaushik Sinha, Pampa Alkuhayli, Abdulaziz Khaled, Usama Mahmoud, Mohamed Metwally BRCM Coll Engn & Technol Bahal 127028 Haryana India JNTUA Coll Engn Pulivendula Dept Elect & Elect Engn Pulivendula 516390 India Graphic Era Deemed Univ Dept Elect Engn Dehra Dun 248002 India Govt Engn Coll Dept Elect Engn Palamu 822118 Jharkhand India BIT Sindri Dept Elect Engn Dhanbad 828123 Jharkhand India KIIT Univ Sch Elect Engn Bhubaneswar 751024 India King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Aswan Univ Fac Energy Engn Elect Engn Dept Aswan 81528 Egypt

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization-sine cosine algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

关键词： cost minimization engineering application Harris Hawk Optimization sine cosine algorithm power flow control

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A New Learnheuristic: Binary SARSA - sine cosine algorithm (BS-SCA) 8th

A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (...

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8th International Conference on Metaheuristics and Nature Inspired Computing (META)

作者： Becerra-Rozas, Marcelo Lemus-Romani, Jose Crawford, Broderick Soto, Ricardo Cisternas-Caneo, Felipe Trujillo Embry, Andres Arnao Molina, Maximo Tapia, Diego Castillo, Mauricio Rubio, Jose-Miguel Pontificia Univ Catolica Valparaiso Valparaiso Chile Pontificia Univ Catolica Chile Santiago Chile Univ Bernardo OHiggins Santiago Chile

ISBN: (纸本)9783030942168;9783030942151

This paper proposes a novel learnheuristic called Binary SARSA - sine cosine algorithm (BS-SCA) for solving combinatorial problems. The BS-SCA is a binary version of sine cosine algorithm (SCA) using SARSA to select a binarization operator. This operator is required due SCA was created to work in continuous domains. The performance of BS-SCA is benchmarked with a Q-learning version of the learnheuristic. The problem tested was the Set Covering Problem and the results show the superiority of our proposal.

关键词： Learnheuristic SARSA sine cosine algorithm Combinatorial problem

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Advanced Ensemble Model for Solar Radiation Forecasting Using sine cosine algorithm and Newton's Laws

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IEEE ACCESS 2021年 9卷 115750-115765页

作者： El-Kenawy, El-Sayed M. Mirjalili, Seyedali Ghoneim, Sherif S. M. Eid, Marwa Metwally El-Said, M. Khan, Zeeshan Shafi Ibrahim, Abdelhameed Delta Higher Inst Engn & Technol DHIET Dept Commun & Elect Mansoura 35111 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Taif Univ Coll Engn Dept Elect Engn At Taif 21944 Saudi Arabia Mansoura Univ Fac Engn Dept Elect Engn Mansoura 35516 Egypt Delta Higher Inst Engn & Technol DHIET Mansoura 35111 Egypt Mustaqbal Univ Coll Engn & Comp Sci Dept Comp Sci Buraydah 52547 Saudi Arabia Mansoura Univ Fac Engn Dept Comp Engn & Control Syst Mansoura 35516 Egypt

As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton's laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent's position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization algorithm (WOA), Genetic algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy sine cosine algorithm with Differential Evolution (HGSCADE), Hybrid Modified sine cosine algorithm with Cuckoo Search algorithm (HMSCACSA), Marine Predators algorithm (MPA), Chimp Optimization algorithm (ChOA), and Slime Mould algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon's rank-sum tests.

关键词： Forecasting Solar radiation Predictive models Prediction algorithms Optimization Machine learning algorithms Genetic algorithms Solar radiation meta-heuristics machine learning K-nearest neighbor sine cosine algorithm

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

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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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