Relevant bibliographies by topics / A sine cosine algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'A sine cosine algorithm'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "A sine cosine algorithm"

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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 unde
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Tang, Junjie, and Lianguo Wang. "Sine Cosine Algorithm for Elite Individual Collaborative Search and Its Application in Mechanical Optimization Designs." Biomimetics 8, no. 8 (2023): 576. http://dx.doi.org/10.3390/biomimetics8080576.

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To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorith
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Pham, Vu Hong Son, and Van Nam Nguyen. "Cement Transport Vehicle Routing with a Hybrid Sine Cosine Optimization Algorithm." Advances in Civil Engineering 2023 (February 17, 2023): 1–15. http://dx.doi.org/10.1155/2023/2728039.

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This study will solve the classical vehicle routing problem, the goal is to generate k trips with the shortest distance for h customers with predetermined locations and needs.. The proposed solution to the classical vehicle routing problem is a hybrid sine cosine algorithm. The sine cosine algorithm is hybridized with the grey wolf optimizer, which is used in combination with the methods of tournament selection, opposition learning, and the mutation and crossover method to build the optimal routing plan for the means of transporting cement. To demonstrate the advantages of the developed hybrid
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Xiao, Shuping, and Ruini Liu. "A multimodal sine-cosine framework for feature selection applications." Intelligent Decision Technologies 19, no. 2 (2024): 1091–104. https://doi.org/10.1177/18724981241295927.

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Not all data features are crucial for uncovering hidden knowledge within various datasets, making the reduction of their dimensional attributes a significant area of interest. This article introduces a novel approach to feature selection within the domain of the sine-cosine algorithm, employing a multimodal optimization strategy. The method is integrated into a wrapper feature selection model that incorporates a range of evaluation function algorithms tied to classifier error rates. The process comprises two key stages: first, the multimodal sine-cosine algorithm is utilized during the feature
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Hennadii, Khudov, Makoveichuk Oleksandr, Khudov Vladyslav, et al. "Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm." Eastern-European Journal of Enterprise Technologies 5, no. 5(119) (2022): 17–24. https://doi.org/10.15587/1729-4061.2022.265775.

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The object of this study is the process of segmentation of images acquired from space optoelectronic surveillance systems. The method to segment images from space optoelectronic surveillance systems based on the Sine-Cosine algorithm involves determining the threshold level; unlike the known ones, the following is carried out in it: – preliminary selection of red-green-blue color space brightness channels in the original image; – calculation of the maximum distance of movement of agents in the image in each brightness channel; – calculation of the values that d
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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 c
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Khudov, Hennadii, Oleksandr Makoveichuk, Vladyslav Khudov, et al. "Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm." Eastern-European Journal of Enterprise Technologies 5, no. 9(119) (2022): 17–24. http://dx.doi.org/10.15587/1729-4061.2022.265775.

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The object of this study is the process of segmentation of images acquired from space optoelectronic surveillance systems. The method to segment images from space optoelectronic surveillance systems based on the Sine-Cosine algorithm involves determining the threshold level; unlike the known ones, the following is carried out in it: – preliminary selection of red-green-blue color space brightness channels in the original image; – calculation of the maximum distance of movement of agents in the image in each brightness channel; – calculation of the values that determine the movement of agents i
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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 prop
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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
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Wang, Pengfei, Yixu Wang, Jiafu Xiang, and Xiaoling Xiao. "Fast Image Encryption Algorithm for Logistics-Sine-Cosine Mapping." Sensors 22, no. 24 (2022): 9929. http://dx.doi.org/10.3390/s22249929.

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Because images are vulnerable to external attacks in the process of network transmission and traditional image encryption algorithms have limitations such as long encryption time, insufficient entropy or poor diffusion of cipher image information when encrypting color images, a fast image encryption algorithm based on logistics-sine-cosine mapping is proposed. The algorithm first generates five sets of encrypted sequences from the logistics-sine-cosine mapping, then uses the order of the encryption sequence to scramble the image pixels and designs a new pixel diffusion network to further impro
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Dissertations / Theses on the topic "A sine cosine algorithm"

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MASERA, MAURIZIO. "Transform algorithms and architectures for video coding." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2709432.

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During the last years, the increasing popularity of very high resolution formats and the growing of video applications have posed critical issues on both the coding efficiency and the complexity of video compression systems. This thesis focuses on the transform coding stage of the most recent video coding technologies, by addressing both the complexity evaluation and the design of custom hardware architectures. First, the thesis thoroughly analyzes the HEVC transform complexity, by relying on the proposed CI metric. A tool-by-tool investigation is performed to quantify the complexity of the tr
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Relton, Samuel. "Algorithms for matrix functions and their Fréchet derivatives and condition numbers." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/algorithms-for-matrix-functions-and-their-frechet-derivatives-and-condition-numbers(f20e8144-1aa0-45fb-9411-ddc0dc7c2c31).html.

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Brown, Susan A. Presmeg Norma C. "The trigonometric connection students' understanding of sine and cosine /." Normal, Ill. : Illinois State University, 2005. http://proquest.umi.com/pqdweb?index=0&did=1251811011&SrchMode=1&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1176384631&clientId=43838.

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Thesis (Ph. D.)--Illinois State University, 2005.<br>Title from title page screen, viewed April 12, 2007. Dissertation Committee: Norma C. Presmeg (chair), John A. Dossey, Michael J. Plantholt. Includes bibliographical references (leaves 253-261) and abstract. Also available in print.
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Martucci, Stephen A. "Symmetric convolution and the discrete sine and cosine transforms : principles and applications." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15038.

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Helmrich, Christian [Verfasser], and Bernd [Gutachter] Edler. "Efficient Perceptual Audio Coding Using Cosine and Sine Modulated Lapped Transforms / Christian Helmrich ; Gutachter: Bernd Edler." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2017. http://d-nb.info/1137055502/34.

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Oyana, Damalie M. "Implementation of a new data stream clustering algorithm using discrete cosine transformed data /." Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1203588021&sid=12&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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John, Julian Daniel. "Acceleration of a Locally Tuned Sine Non Linear Video Enhancement Algorithm on GPGPU." University of Dayton / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1324491495.

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Gambera, Laura Rezzieri [UNESP]. "Soluções quase automórficas para equações diferenciais abstratas de segunda ordem." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/137973.

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Submitted by Laura Rezzieri Gambera null (laura.rgambera@gmail.com) on 2016-04-14T18:58:22Z No. of bitstreams: 1 Dissertação de mestrado - Laura R Gambera.pdf: 953384 bytes, checksum: a1e3addb576bbd6acb2de9e0d0d09394 (MD5)<br>Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-04-18T13:27:01Z (GMT) No. of bitstreams: 1 gambera_lr_me_sjrp.pdf: 953384 bytes, checksum: a1e3addb576bbd6acb2de9e0d0d09394 (MD5)<br>Made available in DSpace on 2016-04-18T13:27:01Z (GMT). No. of bitstreams: 1 gambera_lr_me_sjrp.pdf: 953384 bytes, checksum: a1e3addb576bbd6acb
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OLIVEIRA, Carlos André Carneiro de. "Trigonometria: o radiano e as funções seno, cosseno e tangente." Universidade Federal de Campina Grande, 2014. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/2169.

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Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-11-12T17:19:37Z No. of bitstreams: 1 CARLOS ANDRÉ CARNEIRO DE OLIVEIRA – DISSERTAÇÃO (PPGMat) 2014.pdf: 2129969 bytes, checksum: 19715b30880dbcde6347a169b0fdbdec (MD5)<br>Made available in DSpace on 2018-11-12T17:19:37Z (GMT). No. of bitstreams: 1 CARLOS ANDRÉ CARNEIRO DE OLIVEIRA – DISSERTAÇÃO (PPGMat) 2014.pdf: 2129969 bytes, checksum: 19715b30880dbcde6347a169b0fdbdec (MD5) Previous issue date: 2014-04<br>Este trabalho apresenta um estudo sobre o ensino da trigonometria no ensino médio, contemplando as recomendações
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Souza, Edílson Paiva de. "As funções seno e cosseno: diagnóstico de dificuldades de aprendizagem através de sequências didáticas com diferentes mídias." Pontifícia Universidade Católica de São Paulo, 2010. https://tede2.pucsp.br/handle/handle/10852.

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Made available in DSpace on 2016-04-27T16:57:04Z (GMT). No. of bitstreams: 1 Edilson Paiva de Souza.pdf: 3867466 bytes, checksum: 6a09b6b64515cc8958d8385427c33097 (MD5) Previous issue date: 2010-12-09<br>Secretaria da Educação do Estado de São Paulo<br>This research aims to diagnose the learning difficulties of high school students about the concepts of the trigonometric functions sine and cosine. The research is based on the principles of Didactic Engineering and on the theory of Semiotic Representation Registers created by Raymond Duval. The didactic sequence presented is oriented by analy
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Books on the topic "A sine cosine algorithm"

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8.

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C, Yip P., and Rao K. Ramamohan, eds. Discrete Cosine and Sine Transforms: General Properties, Fast Algorithms and Integer Approximations. Academic Press, 2006.

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Britanak, Vladimir. Discrete cosine and sine transforms: General properties, fast algorithms and integer approximations. Academic, 2007.

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Britanak, Vladimir, and K. R. Rao. Cosine-/Sine-Modulated Filter Banks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61080-1.

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A, Huss Sorin, and SpringerLink (Online service), eds. Constructive Side-Channel Analysis and Secure Design: Third International Workshop, COSADE 2012, Darmstadt, Germany, May 3-4, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Ninul, Anatolij Sergeevič. Tenzornaja trigonometrija: Teorija i prilozenija / Theory and Applications /. Mir Publisher, 2004.

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Ninul, Anatolij Sergeevič. Tensor Trigonometry. Fizmatlit Publisher, 2021.

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Bajpai, Prathu, and Anjali Rawat. Sine Cosine Algorithm for Optimization. Springer, 2023.

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Britanak, Vladimir, and K. R. Rao. Cosine-/Sine-Modulated Filter Banks: General Properties, Fast Algorithms and Integer Approximations. Springer, 2017.

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Britanak, Vladimir, and K. R. Rao. Cosine-/Sine-Modulated Filter Banks: General Properties, Fast Algorithms and Integer Approximations. Springer, 2018.

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Book chapters on the topic "A sine cosine algorithm"

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Sine Cosine Algorithm." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_2.

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AbstractSine cosine algorithm (SCA) [1] is relatively a new algorithm, in the field of meta-heuristic algorithms. SCA is a population-based probabilistic search method that updates the position of search agents in the population using simple concept of trigonometric functions sine and cosine. SCA algorithm is inspired from the periodic property of the sine and cosine functions. The periodicity of the sine and cosine function in the range $$[-1,1]$$ provides great capacity to exploit the search space and helps in maintaining a fine balance between exploration and exploitation. In previous Chap. 1, we have already discussed about the criticality of the exploration and exploitation capabilities of any meta-heuristic algorithm.
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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Conclusion and Further Research Directions." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_6.

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AbstractThe increasing complexity of real-world optimization problems demands fast, robust, and efficient meta-heuristic algorithms. The popularity of these intelligent techniques is gaining popularity day by day among researchers from various disciplines of science and engineering. The sine cosine algorithm is a simple population-based stochastic approach for handling different optimization problems. In this work, we have discussed the basic sine cosine algorithm for continuous optimization problems, the multi-objective sine cosine algorithm for handling multi-objective optimization problems, and the discrete (or binary) versions of sine cosine algorithm for discrete optimization problems. Sine cosine algorithm (SCA) has reportedly shown competitive results when compared to other meta-heuristic algorithms. The easy implementation and less number of parameters make the SCA algorithm, a recommended choice for performing various optimization tasks. In this present chapter, we have studied different modifications and strategies for the advancement of the sine cosine algorithm. The incorporation of concepts like opposition-based learning, quantum simulation, and hybridization with other meta-heuristic algorithms have increased the efficiency and robustness of the SCA algorithm, and meanwhile, these techniques have also increased the application spectrum of the sine cosine algorithm.
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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Advancements in the Sine Cosine Algorithm." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_5.

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AbstractIn the last few decades, the development and advancement of meta-heuristic algorithms have become the focus of the research community as these algorithms face various challenges like, balance between exploration and exploitation, tuning of parameters, getting trapped in local optima, and very slow convergence rate. Sine cosine algorithm (SCA) also faces similar kinds of challenges and sometimes fails to perform effectively in finding the global optimal solution. Sine and cosine are trigonometric operators with a 90$$^\circ $$ phase shift from each other. The range of sine and cosine functions lies in the range $$[-1,1]$$. Sine and cosine functions in the position update equation of SCA help solutions to perform search procedure. However, in some situations, SCA promotes similar solutions in the search space, which results in the loss of diversity in the population, and the search process is susceptible to trapping in the region of local optimum [1]. Motivated by these challenges, SCA has been modified to improve its capability and efficiency in several ways. Several strategies have been employed to alter the basic version of SCA [2], aiming to enhance its effectiveness and optimization capabilities. In this chapter, we will discuss about these modifications and strategies, which have been incorporated into the sine cosine algorithm (SCA) in past few years. Apart from this, we will briefly describe the applications of the modified versions of SCA.
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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Introduction." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_1.

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AbstractDecision-making is a difficult task, and it requires careful analysis of the underlying problem at hand. The presence of various alternative solutions makes the decision-making problem even more difficult as all the available solutions are not optimal. Since resources, time, and money are limited, or even sometimes scarce, the quest for optimal choices is of paramount importance for the welfare of the mankind. Optimization is a mathematical tool and an indispensable part of the decision-making process which assists in finding optimal (or near optimal) solutions from the set of available solutions.
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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Sine Cosine Algorithm for Discrete Optimization Problems." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_4.

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AbstractIn many scenarios, the nature of the decision-making is discrete and we have to deal with a situation where decisions have to be made from the set of discrete choices, or mutually exclusive alternatives. Choices like passing the electric signal versus not passing the electric signal, going upward versus downward, or choosing a certain route over other available routes are discrete in nature. There are many physical systems for which continuous variable modeling is not sufficient to handle the complexity of the physical systems. For instance, communication models, transportation models, finite element analysis, and network routing models are discrete models. The discrete nature of the search space offers the leverage of definiteness, and possibilities for graphical representation of given particular choices. In fact, discrete optimization problems are of paramount importance in various branches of sciences, like decision-making, information systems, and combinatorics. Operation management decision problems, like product distribution, manufacturing facility design, machine sequencing, and production scheduling problems, fall under the purview of discrete optimization problems. Network designing, circuit designing, and automated production systems are also represented as discrete optimization problems. Moreover, the application spectrum of discrete optimization problems includes data mining, data processing, cryptography, graph theory, and many others.
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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Sine Cosine Algorithm for Multi-objective Optimization." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_3.

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AbstractIn many real-world situations, we have to deal with multiple objectives simultaneously in order to make appropriate decisions. The presence of multiple objectives in an optimization problem makes the problem challenging because most of the time these objectives are conflicting in nature. For example, we may want to maximize the return on investment of a portfolio and, on the other hand, minimize the risk associated with the assets in the portfolio. We may want to minimize the cost of a product while maximizing the performance of that particular product. Similarly, there are situations where we may want to maximize more than one objective at a time and minimize multiple objectives for a given optimization problem. For instance, a product manager in an XYZ mobile manufacturing company is supervising the launch of a new smartphone in the market. He/she will have to consider many features and configurations of the smartphone before launching. He/she might have to consider features like the screen resolution, size of the screen, thickness of the phone, camera resolution, battery life, operating system, and even aesthetics of the product. On the other hand, he/she might also want to minimize the amount of labor, time of production, and overall cost associated with the project. He/she knows that the objectives, in this case, are conflicting, and simultaneously achieving every objective in not possible. The solution to this dilemma is to look for some trade-off solutions so that the main motive of the problem can be served.
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Lv, Li, Dengxu He, Miao Lu, and Yundi Rao. "A Quaternion’s Encoding Sine Cosine Algorithm." In Intelligent Computing Theories and Application. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26763-6_68.

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Rawat, Anjali, Shitu Singh, and Jagdish Chand Bansal. "Sine Cosine Algorithm: Introduction and Advances." In The Palgrave Handbook of Operations Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96935-6_13.

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Si, Tapas, and Debolina Bhattacharya. "Sine Cosine Algorithm with Centroid Opposition-Based Computation." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_9.

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Zhao, Yongqi, Feng Zou, and Debao Chen. "A Discrete Sine Cosine Algorithm for Community Detection." In Intelligent Computing Theories and Application. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26763-6_4.

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Conference papers on the topic "A sine cosine algorithm"

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Lu, Qing, Qi Wang, Jinli Fang, Haoran Wang, and Yuanqing Wang. "Improved Sine Cosine Algorithm for Estimating Parameters of Sine Signals." In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). IEEE, 2024. https://doi.org/10.1109/ispcem64498.2024.00013.

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Zhou, Yanqiu, Xiao Wu, Yuxiang Liu, and Xinghe Jiang. "BKA optimization algorithm based on sine-cosine guidelines." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10699037.

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Zhao, Yuntao, and Lixin Fang. "Improved Hyperbolic Sine and Cosine Optimizer that Fuses Golden Sine Algorithm and Tent Chaos Mapping." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864682.

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Mohapatra, Sumant Kumar, Kaliprasanna Swain, Puspanjali Mallik, Sabita Mali, and Farida A. Ali. "Hybridization of Sparrow Search Algorithm and Sine Cosine Algorithm for Epileptic Seizure Detection." In 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON). IEEE, 2024. https://doi.org/10.1109/odicon62106.2024.10797567.

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Mamat, Masrulehsan, Mazidah Mat Rejab, Kamal Z. Zamli, and Ashok Sivaji. "A Hybrid Sine Cosine Algorithm with Cluster Voting for Combinatorial Testing." In 2024 1st International Conference on Cyber Security and Computing (CyberComp). IEEE, 2024. https://doi.org/10.1109/cybercomp60759.2024.10913892.

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Eid, Mahmoud M., Kamal ElDahshan, and Abdelatif H. Abouali. "Missing Data in Smart Cities: An Imputation Algorithm Based on Sine/Cosine Optimization Algorithm." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928154.

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Qiu, Chenye, and Huixing Fang. "An Improved Sine Cosine Algorithm with Chaotic Crossover Operator for Feature Selection." In 2024 International Conference on the Frontiers of Electronic, Electrical and Information Engineering (ICFEEIE). IEEE, 2024. https://doi.org/10.1109/icfeeie64494.2024.00009.

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Wang, Congqian, Shasha Wang, and Xuelei He. "An Improved Sine Cosine Algorithm." In 2023 3rd International Conference on Intelligent Communications and Computing (ICC). IEEE, 2023. http://dx.doi.org/10.1109/icc59986.2023.10421452.

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Zou, Yun-Feng, Juan Zhao, and Zheng-Ming Gao. "Guaranteed Convergence Sine Cosine Algorithm." In EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering. ACM, 2021. http://dx.doi.org/10.1145/3501409.3501586.

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Zhao, Juan, and Zheng-Ming Gao. "Liebovitch map enabled Sine Cosine algorithm." In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE). IEEE, 2021. http://dx.doi.org/10.1109/mlise54096.2021.00064.

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Reports on the topic "A sine cosine algorithm"

1

Horst, John A. Assembly code to compute sine and cosine using the CORDIC algorithm. National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.4480.

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Tang, Ping Tak. Some software implementation of the functions sine and cosine. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7184536.

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