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1
Zhang, Yu-Jun, Yu-Xin Yan, Juan Zhao, and Zheng-Ming Gao. "CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems." PLOS ONE 17, no. 5 (2022): e0263387. http://dx.doi.org/10.1371/journal.pone.0263387.
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Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho.
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Yang, Qingyong, Shu-Chuan Chu, Jeng-Shyang Pan, and Chien-Ming Chen. "Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP." Mathematical Problems in Engineering 2020 (March 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/8184254.
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Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosine algorithm, a multigroup multistrategy SCA algorithm (MMSCA) is proposed in this paper. The algorithm executes multiple populations in parallel, and each population executes a different optimization strategy. Information is exchanged among populations through intergenerational communication. Using 19 different types of test functions, the optimization performance of the algorithm is tested. Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms. At last, it is applied to solving the capacitated vehicle routing problem (CVRP) in transportation. The algorithm can get better results, and the practicability and feasibility of the algorithm are also proved.
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Tahir, Dunia, and Ramzy Ali. "Analysis of Scalability and Sensitivity for Chaotic Sine Cosine Algorithms." Iraqi Journal for Electrical and Electronic Engineering 14, no. 2 (2018): 139–54. http://dx.doi.org/10.37917/ijeee.14.2.6.
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Chaotic Sine-Cosine Algorithms (CSCAs) are new metaheuristic optimization algorithms. However, Chaotic Sine-Cosine Algorithm (CSCAs) are able to manipulate the problems in the standard Sine-Cosine Algorithm (SCA) like, slow convergence rate and falling into local solutions. This manipulation is done by changing the random parameters in the standard Sine-Cosine Algorithm (SCA) with the chaotic sequences. To verify the ability of the Chaotic Sine-Cosine Algorithms (CSCAs) for solving problems with large scale problems. The behaviors of the Chaotic Sine-Cosine Algorithms (CSCAs) were studied under different dimensions 10, 30, 100, and 200. The results show the high quality solutions and the superiority of all Chaotic Sine-Cosine Algorithms (CSCAs) on the standard SCA algorithm for all selecting dimensions. Additionally, different initial values of the chaotic maps are used to study the sensitivity of Chaotic Sine-Cosine Algorithms (CSCAs). The sensitivity test reveals that the initial value 0.7 is the best option for all Chaotic Sine-Cosine Algorithms (CSCAs).
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Parizi, Morteza Karimzadeh, Farshid Keynia, and Amid Khatibi Bardsiri. "HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems." International Journal of Information Technology & Decision Making 20, no. 02 (2021): 775–808. http://dx.doi.org/10.1142/s0219622021500176.
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Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.
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Rizk-Allah, Rizk M. "Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems." Journal of Computational Design and Engineering 5, no. 2 (2017): 249–73. http://dx.doi.org/10.1016/j.jcde.2017.08.002.
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Abstract This paper presents a new algorithm based on hybridizing the sine cosine algorithm (SCA) with a multi-orthogonal search strategy (MOSS), named multi-orthogonal sine cosine algorithm (MOSCA), for solving engineering design problems. The proposed MOSCA integrates the advantages of the SCA and MOSS to eliminate SCA's disadvantages, like unbalanced exploitation and the trapping in local optima. The proposed MOSCA works in two stages, firstly, the SCA phase starts the search process to enhance exploration capability. Secondly, the MOSS phase starts its search from SCA found so far to boost the exploitation tendencies. In this regard, MOSS phase can assist SCA phase to search based on deeper exploration/exploitation patterns as an alternative. Therefore, the MOSCA can be more robust, statistically sound, and quickly convergent. The performance of the MOSCA algorithm is investigated by applying it on eighteen benchmark problems and four engineering design problems. The experimental results indicate that MOSCA is a promising algorithm and outperforms the other algorithms in most cases. Highlights MOSCA is presented to solve design and manufacturing optimization problems efficiently. MOSCA is based on two phases namely, sine cosine algorithm (SCA) and multi-orthogonal search strategy (MOSS). The integrated MOSCA enhances exploration tendency and exploitation capability. The MOSCA can be more robust, statistically sound, and quickly convergent. New approach produced successful results compared to the literature studies.
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Rehman, Muhammad Zubair, Abdullah Khan, Rozaida Ghazali, Muhammad Aamir, and Nazri Mohd Nawi. "A new Multi Sine-Cosine algorithm for unconstrained optimization problems." PLOS ONE 16, no. 8 (2021): e0255269. http://dx.doi.org/10.1371/journal.pone.0255269.
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The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.
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Song, Qi, Yourui Huang, Jiachang Xu, et al. "Research on single cell membrane algorithm and engineering application based on membrane computing theory." Journal of Physics: Conference Series 2387, no. 1 (2022): 012037. http://dx.doi.org/10.1088/1742-6596/2387/1/012037.
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Abstract Membrane computing is a new computing paradigm with great significance in the field of computer science. The Multi-membrane search algorithm (MSA) is proposed based on the membrane computational population optimization theory. It showed excellent performance in the test. This paper further studies the performance characteristics of a single individual (Single Cell Membrane Algorithm, SCA) of MSA. SCA can generate adaptive solution sets for problems of different dimensions. Through transcription and reprocessing rules, new weakly correlated feasible solutions are formed for global search and local exploration. This paper is based on the unimodal Sphere function and the multimodal Rastrigr function, at dim=3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 300, 500, 1000 and Q=1.00, 0.75, 0.50, 0.40, 0.30, 0.20, 0.10, 0.005, 0.025, 0.010, the SCA was optimized for 1000 iterations. Analyze the impact of the key parameter Q of SCA on the search performance of the algorithm in problems of different dimensions. The results show that under the set conditions, SCA has better performance when Q is 0.010 and 0.025 in the unimodal function test. In the multimodal function test, SCA has better performance when dim≤100 and Q≤0.200, and when dim>100 and Q≥0.200. In addition, this paper employs one engineering problem: I-beams to perform engineering tests on SCA and obtain results superior to other algorithms participating in the comparison. The test and comparison results show that SCA can also be used as a derivative algorithm of MSA, and has good performance.
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Jouhari, Hamza, Deming Lei, Mohammed A. A. Al-qaness, Mohamed Abd Elaziz, Ahmed A. Ewees, and Osama Farouk. "Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times." Mathematics 7, no. 11 (2019): 1120. http://dx.doi.org/10.3390/math7111120.
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This paper presents a hybrid method of Simulated Annealing (SA) algorithm and Sine Cosine Algorithm (SCA) to solve unrelated parallel machine scheduling problems (UPMSPs) with sequence-dependent and machine-dependent setup times. The proposed method, called SASCA, aims to improve the SA algorithm using the SCA as a local search method. The SCA provides a good tool for the SA to avoid getting stuck in a focal point and improving the convergence to an efficient solution. SASCA algorithm is used to solve UPMSPs by minimizing makespan. To evaluate the performance of SASCA, a set of experiments were performed using 30 tests for 4 problems. Moreover, the performance of the proposed method was compared with other meta-heuristic algorithms. The comparison results showed the superiority of SASCA over other methods in terms of performance dimensions.
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Zhou, Zhiyu, Ruoxi Zhang, Jianxin Zhang, Yaming Wang, Zefei Zhu, and Chengxia Liu. "Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm." Textile Research Journal 90, no. 17-18 (2020): 2007–21. http://dx.doi.org/10.1177/0040517520908072.
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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.
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Ye, Xiaojia, Zhennao Cai, Chenglang Lu, Huiling Chen, and Zhifang Pan. "Boosted Sine Cosine Algorithm with Application to Medical Diagnosis." Computational and Mathematical Methods in Medicine 2022 (June 22, 2022): 1–21. http://dx.doi.org/10.1155/2022/6215574.
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The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems.
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Sindhu, R., Ruzelita Ngadiran, Yasmin Mohd Yacob, Nik Adilah Hanin Zahri, M. Hariharan, and Kemal Polat. "A Hybrid SCA Inspired BBO for Feature Selection Problems." Mathematical Problems in Engineering 2019 (April 2, 2019): 1–18. http://dx.doi.org/10.1155/2019/9517568.
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Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.
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Zaitchik, Benjamin F., and Matthew Rodell. "Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model." Journal of Hydrometeorology 10, no. 1 (2009): 130–48. http://dx.doi.org/10.1175/2008jhm1042.1.
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Abstract Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring.
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Zhang, Aihua, Danlu Yu, and Zhiqiang Zhang. "TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning." Processes 10, no. 2 (2022): 362. http://dx.doi.org/10.3390/pr10020362.
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In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative fault-diagnosis model namely TLSCA-SVM. Among them, the Sallen–Key bandpass filter circuit and the CSTV filter circuit are used to provide the data for horizontal- and vertical-contrast classification results. Comparing the SCA with the five optimization algorithms, it is concluded that the performance of SCA optimization parameters has certain advantages in the classification accuracy and speed. Additionally, to prove the superiority of the SCA-SVM classification algorithm, the five classification algorithms are compared with the SCA-SVM algorithm. Simulation results showed that the SCA-SVM classification has higher precision and a faster response time compared to the others. After adding the error penalty term to SCA-SVM, TLSCA-SVM requires fewer fault samples to process fault diagnosis. Ultimately, the method which is proposed could not only perform fault diagnosis effectively and quickly, but also could run effectively to achieve the effect of transfer learning in the case of less failure data.
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Brajević, Ivona, Predrag S. Stanimirović, Shuai Li, Xinwei Cao, Ameer Tamoor Khan, and Lev A. Kazakovtsev. "Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems." Mathematics 10, no. 23 (2022): 4555. http://dx.doi.org/10.3390/math10234555.
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Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods.
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Wang, Huimin, Yuelin Gao, and Yahua He. "Hybrid Sine Cosine and Particle Swarm Optimization Algorithm for High-Dimensional Global Optimization Problem and Its Application." Mathematics 12, no. 7 (2024): 965. http://dx.doi.org/10.3390/math12070965.
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Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way.
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Sabery, Ghulam Ali, Ghulam Hassan Danishyar, and Mohammad Arman Osmani. "Evaluation the Performance of Sine Cosine Algorithm in Solving Pressure Vessel Engineering Design Problem." Journal for Research in Applied Sciences and Biotechnology 3, no. 3 (2024): 38–46. http://dx.doi.org/10.55544/jrasb.3.3.8.
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The Sine Cosine Algorithm (SCA) is one of the population-based metaheuristic optimization algorithms inspired by the oscillation and convergence properties of sine and cosine functions. The SCA smoothly transits from exploration to exploitation using adaptive range change in the sine and cosine functions. On the other hand, pressure vessel design is a complex engineering structural optimization problem, which aims to find the best possible design for a vessel that can withstand high pressure. This typically involves optimizing the material, shape, and thickness of the vessel to minimize welding, the material, and forming cost while ensuring it meets safety and performance requirements. This paper evaluates the performance of SCA for solving pressure vessel design problems. The result produced by SCA is compared with the results obtained by other well-known metaheuristic optimization algorithms, namely; ABC, ACO, BBO, CMA-ES, CS, DE, GA, GSA, GWO, HSA, PSO, SSO, TLBO and TSA. The experimental results demonstrated that SCA provides a competitive solution to other metaheuristic optimization algorithms with the advantage of having a simple structured search equation. Moreover, the performance of SCA is checked by different numbers of populations and the results indicated that the best possible population size should be 30 and 40. In addition to this, the SCA search agent success rate is checked for different numbers of populations and results show that the search agent success rate do not exceed 4.2%.
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Rout, Bidyadhar, B. B. Pati, and S. Panda. "Modified SCA algorithm for SSSC damping Controller design in Power System." ECTI Transactions on Electrical Engineering, Electronics, and Communications 16, no. 1 (2017): 46–63. http://dx.doi.org/10.37936/ecti-eec.2018161.171326.
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This paper studies the improvement of transient stability of a single-Machine Infinite-Bus (SMIB) power system using Proportional Derivative (PD) type Static Synchronous Series Compensator (SSSC) and damping controllers. The design problem has been considered as optimisation problem and a modified version of recently proposed Sine Cosine Algorithm (SCA) has been employed for determining the optimal controller parameters. Proposed modified SCA (mSCA) algorithm is first tested using bench mark test functions and compared with SCA, and other heuristic evolutionary optimization algorithms like Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), Gravitational Search algorithm (GSA) and Differential Evolution algorithm to show its superiority. The proposed mSCA algorithm is then applied to optimize simultaneously the PD type lead lag controller parameters pertaining to SSSC and power system stabilizer(PSS). The proposed controller provides sufficient damping for power system oscillation in different operating conditions and disturbances. Results analysis reveal that proposed mSCA technique provides higher effectiveness and robustness in damping oscillations of the power system and increases the dynamic stability more.
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Tian, Ye, Yonghui Huang, Xiaoxu Zhang, and Xiaogang Tang. "Gridless Underdetermined Direction of Arrival Estimation in Sparse Circular Array Using Inverse Beamspace Transformation." Sensors 22, no. 8 (2022): 2864. http://dx.doi.org/10.3390/s22082864.
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Underdetermined DOA estimation, which means estimating more sources than sensors, is a challenging problem in the array signal processing community. This paper proposes a novel algorithm that extends the underdetermined DOA estimation in a Sparse Circular Array (SCA). We formulate this problem as a matrix completion problem. Meanwhile, we propose an inverse beamspace transformation combined with the Gridless SPICE (GLS) algorithm to complete the covariance matrix sampled by SCA. The DOAs are then obtained by solving a polynomial equation with using the Root-MUSIC algorithm. The proposed algorithm is named GSCA. Monte-Carlo simulations are performed to evaluate the GSCA algorithm, the spatial spectrum plots and RMSE curves demonstrated that the GSCA algorithm can give reasonable results of underdetermined DOA estimation in SCA. Meanwhile, the performance of the algorithm under various configurations of SCA is also evaluated. Numerical results indicated that the GSCA algorithm can provide access to solve the DOA estimation problem in Uniform Circular Array (UCA) when random sensor failures occur.
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Alturfi, Alhussein, Sonia Goyal, and Amrit Kaur. "Optimum Design of Scanned Linear Antenna Array Using Sine Cosine Optimization Algorithm." Wasit Journal of Computer and Mathematics Science 2, no. 2 (2023): 92–102. http://dx.doi.org/10.31185/wjcms.135.
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In this article, the side lobe level (SLL) of the radiation pattern is reduced, and the first null beam width (FNBW) is kept constant by synthesizing symmetric scanning Linear Antenna Arrays (LAA), which is done by considering excitation amplitude as the optimization parameter. A Sine cosine algorithm (SCA) is used to achieve this objective. Three different case studies are illustratedin this article to show the effectiveness of SCA in LAA optimization. The results obtained show that the SCA algorithm performs better than Firefly Algorithm (FA), Symbiotic Organisms Search (SOS), and hybrid optimization algorithm based on Grasshopper Optimization Algorithm (GOA) and Antlion Optimization (ALO)
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Liang, Liang. "A Fusion Multiobjective Empire Split Algorithm." Journal of Control Science and Engineering 2020 (December 14, 2020): 1–14. http://dx.doi.org/10.1155/2020/8882086.
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In the last two decades, swarm intelligence optimization algorithms have been widely studied and applied to multiobjective optimization problems. In multiobjective optimization, reproduction operations and the balance of convergence and diversity are two crucial issues. Imperialist competitive algorithm (ICA) and sine cosine algorithm (SCA) are two potential algorithms for handling single-objective optimization problems, but the research of them in multiobjective optimization is scarce. In this paper, a fusion multiobjective empire split algorithm (FMOESA) is proposed. First, an initialization operation based on opposition-based learning strategy is hired to generate a good initial population. A new reproduction of offspring is introduced, which combines ICA and SCA. Besides, a novel power evaluation mechanism is proposed to identify individual performance, which takes into account both convergence and diversity of population. Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems.
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Zheng, Chengfeng, Mohd Shareduwan Mohd Kasihmuddin, Mohd Asyraf Mansor, Ju Chen, and Yueling Guo. "Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm." Mathematics 10, no. 18 (2022): 3368. http://dx.doi.org/10.3390/math10183368.
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The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully studies the main parameters affecting the performance of the sine and cosine algorithm, integrates the reverse learning algorithm, adds an elite opposition solution and forms the hybrid sine and cosine algorithm (hybrid SCA). Combined with the fuzzy k-nearest neighbor method and the hybrid SCA, this paper numerically simulates two-class datasets and multi-class datasets, obtains a large number of numerical results and analyzes the results. The hybrid SCA FKNN proposed in this paper has achieved good accuracy in classification and prediction results under 10 different types of data sets. Compared with SCA FKNN, LSCA FKNN, BA FKNN, PSO FKNN and SSA FKNN, the prediction accuracy is significantly improved. In the Wilcoxon signed rank test with SCA FKNN and LSCA FKNN, the zero hypothesis (significance level 0.05) is rejected and the two classifiers have a significantly different accuracy.
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Ashish, Sharma, Kumar Kar Manoj, and Goud Harsh. "Intelligent metaheuristic algorithm based FOPID controller for CSTR system." International Journal of Advances in Applied Sciences (IJAAS) 14, no. 1 (2025): 60–68. https://doi.org/10.11591/ijaas.v14.i1.pp60-68.
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The purpose of this research is to assess a continuous stirred-tank reactor (CSTR) system's performance. To enhance its performance, a fractional order proportional-integral-derivative (FOPID) controller was employed, necessitating the tuning of independent control parameters. For this purpose, a sine-cosine algorithm (SCA) was introduced to optimize these parameters. The FOPID controller, tuned using the SCA, provides a powerful combination that addresses the complexities of the CSTR system. The fractional-order nature of the FOPID controller allows for superior tuning and robustness, offering enhanced flexibility in adjusting the system’s response characteristics and improving overall control performance. The SCA, known for its effective exploration of the search space through sine and cosine functions, ensures that the controller parameters are optimally selected to enhance the system’s performance by achieving an optimal fitness function. To showcase the effectiveness of the proposed SCA-tuned FOPID controller, comparisons were drawn with other optimization techniques designed for the CSTR system. The study presents time-domain characteristics and frequency responses of the proposed controller. The simulation results demonstrated that the SCA-FOPID controller significantly outperforms the other designed controllers, achieving a 54.07% reduction in the integral of time absolute error (ITAE) compared to genetic algorithm (GA), an 18.64% reduction compared to grey wolf optimizer (GWO), and a 34.79% reduction compared to differential evolution (DE). These significant reductions in ITAE underscore the effectiveness of this approach, highlighting the superior performance and robustness of the SCA-tuned FOPID controller in optimizing the CSTR system.
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Moayedi, A., R. A. Abbaspour, and A. Chehreghan. "A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 737–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-737-2019.
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Abstract. Clustering is an unsupervised learning method that used to discover hidden patterns in large sets of data. Huge data volume and the multidimensionality of trajectories have made their clustering a more challenging task. K-means is a widely used clustering algorithm applied in the trajectory computation field. However, the critical issue with this algorithm is its dependency on the initial values and getting stuck in the local minimum. Meta-heuristic algorithms with the goal of minimizing the cost function of the K-means algorithm can be utilized to address this problem. In this paper, after suggesting a cost function, we compare clustering performance of seven known metaheuristic population-based algorithms including, Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA). The results obtained from the clustering of several data sets with class labels were assessed by internal and external clustering validation indices along with computation time factor. According to the results, PSO, and SCA algorithms show the best results in the clustering regarding the Purity, and computation time metrics, respectively.
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Li, Yu, Yiran Zhao, and Jingsen Liu. "A Levy Flight Sine Cosine Algorithm for Global Optimization Problems." International Journal of Distributed Systems and Technologies 12, no. 1 (2021): 49–66. http://dx.doi.org/10.4018/ijdst.2021010104.
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The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.
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Mishra, Satyasis, Demissie J. Gelmecha, Ram S. Singh, Davinder Singh Rathee, and T. Gopikrishna. "HYBRID WCA–SCA AND MODIFIED FRFCM TECHNIQUE FOR ENHANCEMENT AND SEGMENTATION OF BRAIN TUMOR FROM MAGNETIC RESONANCE IMAGES." Biomedical Engineering: Applications, Basis and Communications 33, no. 03 (2021): 2150017. http://dx.doi.org/10.4015/s1016237221500174.
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Enhancement of image plays an important and vital role in preprocessing the magnetic resonance images (MRI). At the same time, image segmentation techniques are also essential to detect and remove the noise to enhance the quality of MRI to detect the infected regions of the brain tumor. This paper presents a novel image enhancement technique for preprocessing of brain tumor MRI by hybridizing the Water Cycle Algorithm (WCA) and Sine Cosine Algorithm (SCA). The WCA is based on the process of water cycle in rivers and streams flow in the ocean whereas the SCA follows the cyclic form of sine and cosine trigonometric functions, which permits a search agent to be transposed around the desired solution. In fact, the Fuzzy [Formula: see text] means-based segmentation algorithms have proved their ability in automatic detection of the tumor and help doctors and radiologist to diagnose the type of tumor from the MRI, but, some of the FCM-based algorithms fail to remove the required amount of noise from the MRI which restrict doctors to have better segmentation accuracy. A modified fast and robust FCM (MFRFCM) segmentation technique has been proposed to sharpen and remove noise from MRI to detect the brain tumor to have improved accuracy. In this research work, Dataset-255 is considered from the Harvard medical school. The results from the proposed hybrid WCA-SCA technique are compared with WCA, SCA and comparison results are presented. The hybrid WCA+SCA image enhancement technique attains an accuracy of 99.25% for benign tumor and 98.52% for malignant tumor. Further, the results of modified Fast and Robust FCM (MFRFCM) segmentation results are compared with the conventional FCM-based segmentation algorithms. It is observed that the proposed hybrid WCA-SCA image enhancement technique and modified FRFCM Segmentation outperform in terms of computational time and performance accuracy in contrast to the other algorithms.
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Darabi, Hamid, Sedigheh Mohamadi, Zahra Karimidastenaei, et al. "Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models." Soft Computing 25, no. 11 (2021): 7609–26. http://dx.doi.org/10.1007/s00500-021-05721-5.
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AbstractAccurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.
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Khan, Muhammad Fawad, Muhammad Sulaiman, Carlos Andrés Tavera Romero, and Ali Alkhathlan. "Falkner–Skan Flow with Stream-Wise Pressure Gradient and Transfer of Mass over a Dynamic Wall." Entropy 23, no. 11 (2021): 1448. http://dx.doi.org/10.3390/e23111448.
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In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.
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Sharma, Ashish, Manoj Kumar Kar, and Harsh Goud. "Intelligent metaheuristic algorithm based FOPID controller for CSTR system." International Journal of Advances in Applied Sciences 14, no. 1 (2025): 60. https://doi.org/10.11591/ijaas.v14.i1.pp60-68.
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The purpose of this research is to assess a continuous stirred-tank reactor (CSTR) system's performance. To enhance its performance, a fractional-order proportional-integral-derivative (FOPID) controller was employed, necessitating the tuning of independent control parameters. For this purpose, a sine-cosine algorithm (SCA) was introduced to optimize these parameters. The FOPID controller, tuned using the SCA, provides a powerful combination that addresses the complexities of the CSTR system. The fractional-order nature of the FOPID controller allows for superior tuning and robustness, offering enhanced flexibility in adjusting the system’s response characteristics and improving overall control performance. The SCA, known for its effective exploration of the search space through sine and cosine functions, ensures that the controller parameters are optimally selected to enhance the system’s performance by achieving an optimal fitness function. To showcase the effectiveness of the proposed SCA-tuned FOPID controller, comparisons were drawn with other optimization techniques designed for the CSTR system. The study presents time-domain characteristics and frequency responses of the proposed controller. The simulation results demonstrated that the SCA-FOPID controller significantly outperforms the other designed controllers, achieving a 54.07% reduction in the integral of time absolute error (ITAE) compared to genetic algorithm (GA), an 18.64% reduction compared to grey wolf optimizer (GWO), and a 34.79% reduction compared to differential evolution (DE). These significant reductions in ITAE underscore the effectiveness of this approach, highlighting the superior performance and robustness of the SCA-tuned FOPID controller in optimizing the CSTR system.
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H. Suid, M., M. Z. Tumari, and M. A. Ahmad. "A modified sine cosine algorithm for improving wind plant energy production." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 101. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp101-106.
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This paper presents a Modified Sine Cosine Algorithm (M-SCA) to improve the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The two modifications employed to the original SCA are in terms of the updated step size gain and the updated design variable equation. Those modifications are expected to enhance the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-SCA is applied to maximize energy production of a row of ten turbines. The statistical performance analysis shows that the M-SCA provides the highest total energy production as compared to other existing methods.
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Sun, Lichao, Hang Qin, Krzysztof Przystupa, et al. "A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques." Energies 15, no. 10 (2022): 3485. http://dx.doi.org/10.3390/en15103485.
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Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.
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Zhang, Hai Yang, Xun Li, Xiang Ke Wang, and Meng Zhang. "Grid-Based Clustering Algorithm for Sensing." Applied Mechanics and Materials 203 (October 2012): 257–62. http://dx.doi.org/10.4028/www.scientific.net/amm.203.257.
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Energy efficiency is considered as a challenge in Wireless Sense Networks because of the limited energy. In this paper a novel grid-clustering sensing algorithm, the SCA (the sensing clustering algorithm) is proposed in order to minimize energy expenditure and maximize network lifetime. Different to all conventional methods, the proposed algorithm clusters nodes depending on the sensing ability, and forms a comprehensive covered and fully connected network. Both of the theoretical analyses and the simulation indicate that the SCA reduces the energy consumption effectively.
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Huang, J. C., S. J. Kao, M. L. Hsu, and Y. A. Liu. "Influence of Specific Contributing Area algorithms on slope failure prediction in landslide modeling." Natural Hazards and Earth System Sciences 7, no. 6 (2007): 781–92. http://dx.doi.org/10.5194/nhess-7-781-2007.
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Abstract. This study anatomized algorithm effects of specific contributing area (SCA) on soil wetness estimation, consequently landslide prediction, in SHALSTAB. A subtropical mountainous catchment during three typhoon invasions is targeted. The peak 2-day rainfall intensity of the three typhoons: Haitang, Mindulle and Herb are 144, 248 and 327 mm/day, respectively. We use modified success rate (MSR) to retrieve the most satisfying mean condition for model parameters in SHALSTAB at three rainfall intensities and respective pre-typhoon NDVI themes. Simulation indicates that algorithm affects the prediction of landslide susceptibility (i.e. FS, Factor of Safety) significantly. Based on fixed NDVI and the mean condition, we simulate by using full scale rainfall intensity from 0 to 1200 mm/day. Simulations show that predicted unstable area coverage increases non-linearly as rainfall intensity increases for all algorithms yet with different increasing trends. Compared to Dinf, D8 always gives lower coverage of predicted unstable area during three typhoons. By contrast, FD8 gives higher coverage areas. The absolute difference (compared to Dinf) in predicted unstable area ranges from ~−3% to +4% (percent watershed area). The relative difference (compared to Dinf) ranges from −15% to as high as +40%. The maximum absolute and relative differences in unstable area prediction occur around the condition of 100–300 mm/day, which is common in subtropical mountainous region. Theoretical relationship among slope, rainfall intensity, SCA and FS value was derived in which FS values are very sensitive to algorithms in the field of slope from 37 to 52degree. Results imply any comparison among SCA-related landslide models or engineering application of rainfall return period analysis must base on the same algorithm to obtain comparable results. This study clarifies the SCA algorithm effect on FS prediction and deepens our understanding on landslide modeling.
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Li, Yuping. "Similar Classification Algorithm for Educational and Teaching Knowledge Based on Machine Learning." Wireless Communications and Mobile Computing 2022 (May 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/7222236.
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From ancient times, machines did adhere to the commands that a human or a user prepared. According to the program, the machines are controlled by implementing machine learning (ML). It plays a significant part in the development of information technology (IT) companies and the rise of the education system. Using stored memories, people learn new things, making them feel better than before. Machines are pretty different from human knowledge. Instead of using memory power, they use statistical comparison to analyze the data. Here, the amount of data is stored in a database, and according to the reaction received from the user, it gets additional data to create new data. For example, once a person hears music using the application, they will hear repeated music before further entry. In this case, the application is working based on the machine learning algorithm. First, it collects the information from the user, and then, it uses the same information (data) to make the user’s work more efficient when they return. The existing system like Support Vector Machine (SVM) and learning management system approaches the necessity and development of the higher education system using machine learning algorithms. This proposed system focuses on classifying education and teaching knowledge by implementing the machine learning-based similar classification algorithm (ML-SCA). ML-SCA focuses on classifying similar teaching videos and the recommendations to improve the teaching and academic knowledge for the teachers and the students. ML-SCA is compared with the existing neural network and K -means algorithms. Based on the efficiency results, it is observed that the proposed ML-SCA has achieved 92% higher than the existing algorithms.
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ALLEFELD, CARSTEN. "ABOUT THE DERIVATION OF THE SCA ALGORITHM." International Journal of Bifurcation and Chaos 16, no. 12 (2006): 3705–6. http://dx.doi.org/10.1142/s0218127406017099.
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An approach to multivariate phase synchronization analysis in the form of a Synchronization Cluster Analysis (SCA) was introduced in [Allefeld & Kurths, 2004]. A statistical model of a synchronization cluster was described, and an abbreviated instruction on how to apply this model to empirical data was given, while an implementation of the corresponding algorithm was (and is) available from the authors. In this letter, the complete details on how the data analysis algorithm is to be derived from the model are filled in.
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Pan, Jeng-Shyang, Si-Qi Zhang, Shu-Chuan Chu, Chia-Cheng Hu, and Jie Wu. "Efficient FPGA Implementation of Sine Cosine Algorithm using High Level Synthesis." Journal of Internet Technology 25, no. 6 (2024): 865–76. http://dx.doi.org/10.70003/160792642024112506007.
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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.
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Xu, Yubao, and Jinzhong Zhang. "A Hybrid Nonlinear Whale Optimization Algorithm with Sine Cosine for Global Optimization." Biomimetics 9, no. 10 (2024): 602. http://dx.doi.org/10.3390/biomimetics9100602.
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The whale optimization algorithm (WOA) is constructed on a whale’s bubble-net scavenging pattern and emulates encompassing prey, bubble-net devouring prey, and stochastic capturing for prey to establish the global optimal values. Nevertheless, the WOA has multiple deficiencies, such as restricted precision, sluggish convergence acceleration, insufficient population variety, easy premature convergence, and restricted operational efficiency. The sine cosine algorithm (SCA) constructed on the oscillation attributes of the cosine and sine coefficients in mathematics is a stochastic optimization methodology. The SCA upgrades population variety, amplifies the search region, and accelerates international investigation and regional extraction. Therefore, a hybrid nonlinear WOA with SCA (SCWOA) is emphasized to estimate benchmark functions and engineering designs, and the ultimate intention is to investigate reasonable solutions. Compared with other algorithms, such as BA, CapSA, MFO, MVO, SAO, MDWA, and WOA, SCWOA exemplifies a superior convergence effectiveness and greater computation profitability. The experimental results emphasize that the SCWOA not only integrates investigation and extraction to avoid premature convergence and realize the most appropriate solution but also exhibits superiority and practicability to locate greater computation precision and faster convergence speed.
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Parida, Bivasa, Amiya Rath, Bibudhendu Pati, et al. "SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu." Computer Science and Information Systems, no. 00 (2024): 18. http://dx.doi.org/10.2298/csis230923018p.
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The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.
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Kuwil, Farag Homed Ali. "Kuwil method for spectral clustering algorithm." Global Journal of Computer Sciences: Theory and Research 7, no. 2 (2017): 102–11. http://dx.doi.org/10.18844/gjcs.v7i2.2711.
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The open issues and challenges that exist while using the spectral clustering algorithm (SCA) have led to its limited spread in practical life. This paper proposes to find an easier, faster and more accurate method to implement SCA that will lead to its wide use by statisticians, researchers, institutions and others. I suggest a new method called ‘Kuwil method’ for SCA on any dataset points without needing estimation or evaluation of any parameters or the use of linear algebra, not even the k_mean algorithm. The main aim is to apply an algorithm that relies on distance laws among points only. The algorithm by the Kuwil method has been applied a number of times on real data from the warehouse European Economic Association (http://ec.europa.eu/eurostat/data/database) and on unreal data. The results were highly efficient in terms of time, effort and simplification. It eliminates the problem of parameters and increases the effectiveness to give static results obtained from the first execution. No errors were seen from functions in the MATLAB language such as eigenvalues, eigenvector and k_mean.
 Keywords: Spectral clustering, Kuwil method.
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Song, Qi, Yourui Huang, Wenhao Lai, et al. "FPGA Hardware Realization of Membrane Calculation Optimization Algorithm with Great Parallelism." Symmetry 14, no. 10 (2022): 2199. http://dx.doi.org/10.3390/sym14102199.
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Aiming to investigate the disadvantage of the optimization algorithm of membrane computing (a P system) in which it is difficult to take advantage of parallelism in MATLAB, leading to a slow optimization speed, a digital-specific hardware solution (field-programmable gate array, FPGA) is proposed to design and implement the single-cell-membrane algorithm (SCA). Because the SCA achieves extensive global searches by the symmetric processing of the solution set, with independent and symmetrically distributed submembrane structures, the FPGA-hardware-based design of the SCA system includes a control module, an HSP module, an initial value module, a fitness module, a random number module, and multiple submembrane modules with symmetrical structures. This research utilizes the inherent parallel characteristics of the FPGA to achieve parallel computations of multiple submembrane modules with a symmetric structure inside the SCA, and it achieves a high degree of parallelism of rules inside the modules by using a non-blocking allocation. This study uses the benchmark Sphere function to verify the performance of the FPGA-designed SCA system. The experimental results show that, when the FPGA platform and the MATLAB platform obtain a similar calculation accuracy, the average time-consuming of the FPGA is 0.00041 s, and the average time-consuming of MATLAB is 0.0122 s, and the calculation speed is improved by nearly 40 times. This study uses the FPGA design to implement the SCA, and it verifies the advantages of the membrane-computing maximum-parallelism theory and distributed structures in computing speed. The realization platform of membrane computing is expanded, which provides a theoretical basis for further development of the distributed computing model of population cells.
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Rousselot, Nathan, Karine Heydemann, Loïc Masure, and Vincent Migairou. "Scoop: An Optimization Algorithm for Profiling Attacks against Higher-Order Masking." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 3 (2025): 56–80. https://doi.org/10.46586/tches.v2025.i3.56-80.
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In this paper we provide new theoretical and empirical evidences that gradient-based deep learning profiling attacks (DL-SCA) suffer from masking schemes. This occurs through an initial stall of the learning process: the so-called plateau effect. To understand why, we derive an analytical expression of a DL-SCA model targeting simulated traces which enables us to study an analytical expression of the loss. By studying the loss landscape of this model, we show that not only do the magnitudes of the gradients decrease as the order of masking increases, but the loss landscape also exhibits a prominent saddle point interfering with the optimization process. From these observations, we (1) propose the usage of a second-order optimization algorithm mitigating the impact of low-gradient areas. In addition, we show how to leverage the intrinsic sparsity of valuable information in SCA traces to better pose the DL-SCA problem. To do so, we (2) propose to use the implicit regularization properties of the sparse mirror descent. These propositions are gathered in a new publicly available optimization algorithm, Scoop. Scoop combines second-order derivative of the loss function in the optimization process, with a sparse stochastic mirror descent. We experimentally show that Scoop pushes further the current limitations of DL-SCA against simulated traces, and outperforms the state-of-theart on the ASCADv1 dataset in terms of number of traces required to retrieve the key, perceived information and plateau length. Scoop also performs the first nonworst- case attack on the ASCADv2 dataset. On simulated traces, we show that using Scoop reduces the DL-SCA time complexity by the equivalent of one masking order.
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Rosli, Siti Julia, Hasliza A. Rahim, Khairul Najmy Abdul Rani, et al. "A Hybrid Modified Method of the Sine Cosine Algorithm Using Latin Hypercube Sampling with the Cuckoo Search Algorithm for Optimization Problems." Electronics 9, no. 11 (2020): 1786. http://dx.doi.org/10.3390/electronics9111786.
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The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.
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Ghayad, Mohamed S., Niveen M. Badra, Almoataz Y. Abdelaziz, and Mahmoud A. Attia. "Reactive power control to enhance the VSC-HVDC system performance under faulty and normal conditions." International Journal of Applied Power Engineering 8, no. 2 (2019): 145–58. https://doi.org/10.11591/ijape.v8.i2.pp145-158.
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This paper studied the reactive power control of the voltage source converters based high-voltage direct current system (VSC-HVDC). PI (proportional & integration) controller was used in this work to enhance the dynamic response of the system. Gravitational search algorithm (GSA) and sine cosine algorithm (SCA) are used to get optimal parameters of the PI controller. GSA algorithm is based on the gravity law for Newton while SCA depends on mathematical model based on cosine and sine functions. These algorithms have an efficient global Search capability. The VSC-HVDC is exposed to different disturbances for checking the controller robustness. First disturbance was applying three phase faults on the system. While the second one was applying a step change in AC voltage. Finally, applying step change in regulators reference values. Simulation results proved the controller superiority also verified the enhancement of the system dynamic response.
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Mohamed, S. Ghayad, M. Badra Niveen, Y. Abdelaziz Almoataz, and A. Attia Mahmoud. "Reactive power control to enhance the VSC-HVDC system performance under faulty and normal conditions." International Journal of Applied Power Engineering 8, no. 2 (2019): 145~158. https://doi.org/10.5281/zenodo.7354422.
Full textAbstract:
This paper studied the reactive power control of the voltage source converters based high-voltage direct current system (VSC-HVDC). PI (proportional & integration) controller was used in this work to enhance the dynamic response of the system. Gravitational search algorithm (GSA) and sine cosine algorithm (SCA) are used to get optimal parameters of the PI controller. GSA algorithm is based on the gravity law for Newton while SCA depends on mathematical model based on cosine and sine functions. These algorithms have an efficient global Search capability. The VSC-HVDC is exposed to different disturbances for checking the controller robustness. First disturbance was applying three phase faults on the system. While the second one was applying a step change in AC voltage. Finally, Applying step change in regulators reference values. Simulation results proved the controller superiority also verified the enhancement of the system dynamic response.
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Mohamed, S. Ghayad, M. Badra Niveen, Y. Abdelaziz Almoataz, and A. Attia Mahmoud. "Reactive power control to enhance the VSC-HVDC system performance under faulty and normal conditions." International Journal of Applied Power Engineering 8, no. 2 (2019): 145~158. https://doi.org/10.5281/zenodo.7354476.
Full textAbstract:
This paper studied the reactive power control of the voltage source converters based high-voltage direct current system (VSC-HVDC). PI (proportional & integration) controller was used in this work to enhance the dynamic response of the system. Gravitational search algorithm (GSA) and sine cosine algorithm (SCA) are used to get optimal parameters of the PI controller. GSA algorithm is based on the gravity law for Newton while SCA depends on mathematical model based on cosine and sine functions. These algorithms have an efficient global Search capability. The VSC-HVDC is exposed to different disturbances for checking the controller robustness. First disturbance was applying three phase faults on the system. While the second one was applying a step change in AC voltage. Finally, Applying step change in regulators reference values. Simulation results proved the controller superiority also verified the enhancement of the system dynamic response.
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Ge, Weili, Zhengyu Zhu, Zhongyong Wang, and Zhengdao Yuan. "AN-Aided Transmit Beamforming Design for Secured Cognitive Radio Networks with SWIPT." Wireless Communications and Mobile Computing 2018 (August 13, 2018): 1–13. http://dx.doi.org/10.1155/2018/6956313.
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We investigate multiple-input single-output secured cognitive radio networks relying on simultaneous wireless information and power transfer (SWIPT), where a multiantenna secondary transmitter sends confidential information to multiple single-antenna secondary users (SUs) in the presence of multiple single-antenna primary users (PUs) and multiple energy-harvesting receivers (ERs). In order to improve the security of secondary networks, we use the artificial noise (AN) to mask the transmit beamforming. Optimization design of AN-aided transmit beamforming is studied, where the transmit power of the information signal is minimized subject to the secrecy rate constraint, the harvested energy constraint, and the total transmit power. Based on a successive convex approximation (SCA) method, we propose an iterative algorithm which reformulates the original problem as a convex problem under the perfect channel state information (CSI) case. Also, we give the convergence of the SCA-based iterative algorithm. In addition, we extend the original problem to the imperfect CSI case with deterministic channel uncertainties. Then, we study the robust design problem for the case with norm-bounded channel errors. Also, a robust SCA-based iterative algorithm is proposed by adopting the S-Procedure. Simulation results are presented to validate the performance of the proposed algorithms.
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Templos-Santos, Juan, Omar Aguilar-Mejia, Edgar Peralta-Sanchez, and Raul Sosa-Cortez. "Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms." Algorithms 12, no. 3 (2019): 54. http://dx.doi.org/10.3390/a12030054.
Full textAbstract:
This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.
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Kim, Sungil, and Kyungbook Lee. "Application of Spectral Clustering Algorithm to ES-MDA with DCT for History Matching of Gas Channel Reservoirs." Energies 12, no. 22 (2019): 4394. http://dx.doi.org/10.3390/en12224394.
Full textAbstract:
History matching is a calibration of reservoir models according to their production history. Although ensemble-based methods (EBMs) have been researched as promising history matching methods, reservoir parameters updated using EBMs do not have ideal geological features because of a Gaussian assumption. This study proposes an application of spectral clustering algorithm (SCA) on ensemble smoother with multiple data assimilation (ES-MDA) as a parameterization technique for non-Gaussian model parameters. The proposed method combines discrete cosine transform (DCT), SCA, and ES-MDA. After DCT is used to parameterize reservoir facies to conserve their connectivity and geometry, ES-MDA updates the coefficients of DCT. Then, SCA conducts a post-process of rock facies assignment to let the updated model have discrete values. The proposed ES-MDA with SCA and DCT gives a more trustworthy history matching performance than the preservation of facies ratio (PFR), which was utilized in previous studies. The SCA considers a trend of assimilated facies index fields, although the PFR classifies facies through a cut-off with a pre-determined facies ratio. The SCA properly decreases uncertainty of the dynamic prediction. The error rate of ES-MDA with SCA was reduced by 42% compared to the ES-MDA with PFR, although it required an extra computational cost of about 9 min for each calibration of an ensemble. Consequently, the SCA can be proposed as a reliable post-process method for ES-MDA with DCT instead of PFR.
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Morales Romero, José de Jesús, Mario Alfredo Reyes Barranca, David Tinoco Varela, Luis Martin Flores Nava, and Emilio Rafael Espinosa Garcia. "SCA-Safe Implementation of Modified SaMAL2R Algorithm in FPGA." Micromachines 13, no. 11 (2022): 1872. http://dx.doi.org/10.3390/mi13111872.
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Cryptographic algorithms (RSA, DSA, and ECC) use modular exponentiation as part of the principal operation. However, Non-profiled Side Channel Attacks such as Simple Power Analysis and Differential Power Analysis compromise cryptographic algorithms that use such operation. In this work, we present a modification of a modular exponentiation algorithm implemented in programmable devices, such as the Field Programmable Gate Array, for which we use Virtex-6 and Artix-7 evaluation boards. It is shown that this proposal is not vulnerable to the attacks mentioned previously. Further, a comparison was made with other related works, which use the same family of FPGAs. These comparisons show that this proposal not only defeats physical attack but also reduces the number of resources. For instance, the present work reduces the Look-Up Tables by 3550 and the number of Flip-Flops was decreased by 62,583 compared with other works. Besides, the number of memory blocks used is zero in the present work, in contrast with others that use a large number of blocks. Finally, the clock cycles (latency) are compared in different programmable devices to perform operations.
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Patgiri, Chayashree, and Amrita Ganguly. "Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method." Asian Pacific Journal of Health Sciences 9, no. 4 (2022): 165–70. http://dx.doi.org/10.21276/apjhs.2022.9.4.33.
Full textAbstract:
Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the detection of sickle cells, supervised classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used. Here, geometric features of healthy and unhealthy RBCs are calculated and applied to these classifiers. In this approach, performance is found slightly greater in SVM classifier than the ANN classifier trained with scaled conjugate gradient back-propagation (BP) algorithm and with hidden layer of ten neurons. The proposed approach achieves a maximum of 99.2% accuracy with SVM classifier. The performance is also studied for seven different training algorithms in the ANN classifier by varying the numbers of hidden layer neurons. Comparative analysis of the performances of these algorithms shows that, resilient BP algorithm and 10 numbers of hidden neurons gave moderately better performance in ANN with 99% accuracy. ANN and SVM classifier with adaptive thresholding and active contour technique is an efficient approach for the classification of patients with SCA.
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Ye, Dan, Yu Liu, Shan Zhang, et al. "An improved sine cosine algorithm for assignment problem." Journal of Physics: Conference Series 2031, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1742-6596/2031/1/012057.
Full textAbstract:
Abstract The assignment problem is a NP-hard combinatorial optimization problem, where assignees are being assigned to perform tasks. This paper presents an improved sine and cosine algorithm (ISCA) to solve this problem. Nonlinear decreasing inertia weight, chaotic map and greedy strategy are added to the original sine cosine algorithm (SCA) to enhance the ability of focusing on optimal and avoiding local optima. Simulation results show that the proposed algorithm can get more competitive solutions when compared with differential evolution (DE), particle swarm optimization (PSO) and original SCA.