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Relevant bibliographies by topics / Money Particle Swarm Optimization

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Academic literature on the topic 'Money Particle Swarm Optimization'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Money Particle Swarm Optimization"

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Guo, Aifang, Lina Zhu, and Lingjie Chang. "An Optimization Method for Enterprise Resource Integration Based on Improved Particle Swarm Optimization." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/6928989.

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An enterprise’s development and growth are inextricably linked to rational and efficient resource integration and optimization. This study focuses on the reorganization and integration of industrial elements inside the firm from the standpoint of resource integration. The ideal resource integration strategy is investigated by integrating the industrial parts of a certain enterprise in order to increase the efficiency of project completion and lower enterprise expenses. The enterprise’s internal material and human resources are limited, but it is frequently necessary to execute numerous activit

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Tang, Longjiao. "Logistics Path Planning based on Improved Particle Swarm Optimization Algorithm." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1276–83. https://doi.org/10.12694/scpe.v26i3.4205.

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In order to solve the problem of vehicle path scheduling management more reasonably, the author proposes a logistics path planning research based on improved particle swarm optimization algorithm. The author introduces a particle swarm optimization algorithm that incorporates a dynamic monkey jumping mechanism. Initially, dynamic population grouping is used to assign varying dynamic inertia weights, enhancing the algorithm’s speed. Subsequently, the monkey jumping mechanism is added to ensure global convergence. This enhanced algorithm was then tested on two logistics distribution path optimiz

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Merlina, Nita, Ade Chandra, and Nissa Almira Mayangky. "PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES." INTI Nusa Mandiri 18, no. 2 (2024): 115–21. http://dx.doi.org/10.33480/inti.v18i2.4982.

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In the digital age emerging currencies using digital technology called currency crypto money. Many people use cryptocurrencies to invest. This triggered the sentiment in society on social media twitter, there are positive opinions and there are negative opinions. The purpose of this study is to determine the public sentiment regarding the review of crypto currency and then classify it into two sentiments, namely positive and negative sentiments. The classifier method used is Naïve Bayes, Naïve Bayes is a good classifier method but has shortcomings in the selection of features therefore Particl

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Venkataiah, V., M. Nagaratna, and Ramakanta Mohanty. "Application of Chaotic Increasing Linear Inertia Weight and Diversity Improved Particle Swarm Optimization to Predict Accurate Software Cost Estimation." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 154–60. http://dx.doi.org/10.37391/ijeer.100218.

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Nowadays usage of software products is increases exponential in different areas in society, accordingly, the development of software products as well increases by the software organizations, but they are unable to focus to predict effective techniques for planning resources, reliable design, and estimation of time, budget, and high quality at the preliminary phase of the development of the product lifecycle. Consequently, it delivered improper software products. Hence, a customer loses the money, time, and not belief on the company as well as effort of teamwork will be lost. We need an efficie

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Yusuf, Priyo Anggodo, and Firdaus Mahmudy Wayan. "A Novel Forecasting Based on Automatic-optimized Fuzzy Time Series." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1809–17. https://doi.org/10.12928/TELKOMNIKA.v16i4.8430.

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In this paper, we propose a new method for forecasting based on automatic-optimized fuzzy time series to forecast Indonesia Inflation Rate (IIR). First, we propose the forecasting model of two-factor highorder fuzzy-trend logical relationships groups (THFLGs) for predicting the IIR. Second, we propose the interval optimization using automatic clustering and particle swarm optimization (ACPSO) to optimize the interval of main factor IIR and secondary factor SF, where SF = {Customer Price Index (CPI), the Bank of Indonesia (BI) Rate, Rupiah Indonesia /US Dollar (IDR/USD) Exchange rate, Money Sup

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Tatale, Subhash, and Vudatha Chandra Prakash. "Automatic Generation and Optimization of Combinatorial Test Cases from UML Activity Diagram Using Particle Swarm Optimization." Ingénierie des systèmes d information 27, no. 1 (2022): 49–59. http://dx.doi.org/10.18280/isi.270106.

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Generation of test cases is one of the essential activities of the software testing process. The process of executing a programme to identify defects to improve the system's quality is known as software testing. Manually writing test cases takes time, effort, and money. On the other hand, generating test cases automatically is the solution to this problem. For this automation process, a model-based test case generation technique would be acceptable. A model is usually required to generate test cases in the model-based testing technique. Nowadays, researchers have relied on the activity diagram

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Rohmayani, Dini. "ANALYSIS OF STUDENT TUITION FEE PAY DELAY PREDICTION USING NAIVE BAYES ALGORITHM WITH PARTICLE SWARM OPTIMATION OPTIMAZATION (CASE STUDY : POLITEKNIK TEDC BANDUNG)." Jurnal Teknologi Informasi dan Pendidikan 13, no. 2 (2020): 1–8. http://dx.doi.org/10.24036/tip.v13i2.317.

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One source of funds that plays a very important role in education or teaching and learning activities is the Donation of Education Development or tuition fee. The problem faced by TEDC Polytechnic of Bandung is based on historical data from the financial department there are still many students who are late in making tuition payments, for that the authors make an analysis in predicting late payment of fees by using the Naive Bayes algorithm which is compared with Particle Swarm Optimization (PSO) with the purpose of determining the classification pattern is right or late and to find out what i

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Jalaee, Sayyed Abdolmajid, Alireza Shakibaei, Hamid Reza Horry, et al. "A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran." MethodsX 8 (2021): 101226. http://dx.doi.org/10.1016/j.mex.2021.101226.

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Omran, Sherin M., Wessam H. El-Behaidy, and Aliaa A. A. Youssif. "Optimization of Cryptocurrency Algorithmic Trading Strategies Using the Decomposition Approach." Big Data and Cognitive Computing 7, no. 4 (2023): 174. http://dx.doi.org/10.3390/bdcc7040174.

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A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trad

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Xie, Wangsong. "Interbank Offered Rate Based on Artificial Intelligence Algorithm." Mathematical Problems in Engineering 2021 (May 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/9931539.

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Interbank offer rate is the interest rate at which banks lend money to each other in the money market. As a market-oriented core interest rate, Shibor can accurately and timely reflect the capital supply and demand relationship in the money market, and its changes will quickly transmit and affect China’s financial market. Therefore, the purpose of this paper is to predict and study the fluctuation and trend of Shibor. In this paper, the overnight varieties of Shibor were studied and predicted from two time dimensions, namely, daily fluctuation and monthly trend. In the prediction of overnight

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Dissertations / Theses on the topic "Money Particle Swarm Optimization"

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Devarakonda, SaiPrasanth. "Particle Swarm Optimization." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335827032.

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Al-kazemi, Buthainah Sabeeh No'man. "Multiphase particle swarm optimization." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.

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Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.

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An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (PO

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Djaneye-Boundjou, Ouboti Seydou Eyanaa. "Particle Swarm Optimization Stability Analysis." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386413941.

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Czogalla, Jens. "Particle swarm optimization for scheduling problems." Aachen Shaker, 2010. http://d-nb.info/1002307813/04.

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Jin, Nanbo. "Particle swarm optimization in engineering electromagnetics." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1481677311&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Brits, Riaan. "Niching strategies for particle swarm optimization." Diss., Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-02192004-143003.

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Lapizco-Encinas, Grecia C. "Cooperative Particle Swarm Optimization for Combinatorial Problems." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9901.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2009.<br>Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.

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Yao, Wang. "Particle swarm optimization aided MIMO transceiver design." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/301206/.

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In this treatise, we design Particle Swarm Optimization (PSO) aided MIMO transceivers. The employment of multiple antennas leads to the concept of multiple-input multiple-output (MIMO) systems, which constitute an effective way of achieving an increased capacity. When multiple antennas are employed at the Base Station (BS), it is possible to employ Multiuser Detection (MUD) in the uplink. However, in the downlink (DL), due to the size as well as power consumption constraints of mobile devices, so-called Multiuser Transmission (MUT) techniques may be employed at the BS for suppressing the multi

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Books on the topic "Money Particle Swarm Optimization"

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Lazinica, Aleksandar. Particle swarm optimization. InTech, 2009.

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Mercangöz, Burcu Adıgüzel, ed. Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6.

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Couceiro, Micael, and Pedram Ghamisi. Fractional Order Darwinian Particle Swarm Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19635-0.

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Mikki, Said M., and Ahmed A. Kishk. Particle Swarm Optimization: A Physics-Based Approach. Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01704-9.

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Olsson, Andrea E. Particle swarm optimization: Theory, techniques, and applications. Nova Science Publishers, 2010.

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1974-, Parsopoulos Konstantinos E., and Vrahatis Michael N. 1955-, eds. Particle swarm optimization and intelligence: Advances and applications. Information Science Reference, 2010.

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Parsopoulos, Konstantinos E. Particle swarm optimization and intelligence: Advances and applications. Information Science Reference, 2010.

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Kiranyaz, Serkan, Turker Ince, and Moncef Gabbouj. Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37846-1.

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Choi-Hong, Lai, and Wu Xiao-Jun, eds. Particle swarm optimisation: Classical and quantum perspectives. CRC Press, 2011.

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Clerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2010.

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Book chapters on the topic "Money Particle Swarm Optimization"

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Du, Ke-Lin, and M. N. S. Swamy. "Particle Swarm Optimization." In Search and Optimization by Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_9.

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Bansal, Jagdish Chand. "Particle Swarm Optimization." In Studies in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91341-4_2.

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Kaveh, A. "Particle Swarm Optimization." In Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05549-7_2.

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Gazi, Veysel, and Kevin M. Passino. "Particle Swarm Optimization." In Swarm Stability and Optimization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18041-5_12.

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Awange, Joseph L., Béla Paláncz, Robert H. Lewis, and Lajos Völgyesi. "Particle Swarm Optimization." In Mathematical Geosciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67371-4_6.

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Zeugmann, Thomas, Pascal Poupart, James Kennedy, et al. "Particle Swarm Optimization." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_630.

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Kaveh, A. "Particle Swarm Optimization." In Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46173-1_2.

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Shanthi, M. B., D. Komagal Meenakshi, and Prem Kumar Ramesh. "Particle Swarm Optimization." In Advances in Swarm Intelligence for Optimizing Problems in Computer Science. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429445927-5.

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Chopard, Bastien, and Marco Tomassini. "Particle Swarm Optimization." In Natural Computing Series. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93073-2_6.

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Luz, Eduardo Fávero Pacheco da, José Carlos Becceneri, Stephan Stephany, Haroldo Fraga de Campos Velho, and Antônio José da Silva Neto. "Particle Swarm Optimization." In Computational Intelligence Applied to Inverse Problems in Radiative Transfer. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43544-7_10.

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Conference papers on the topic "Money Particle Swarm Optimization"

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Ravivarman, G., Abhiraj Malhotra, B. Reddy, Rajalakshmi G, Satish Kumar R, and V. A. Mishra. "A Hybrid Approach Using Spider Monkey and Tiered Particle Swarm Optimization for IoT Disturbance Recognition." In 2024 IEEE 2nd International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP). IEEE, 2024. https://doi.org/10.1109/ihcsp63227.2024.10959916.

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Huseyinov, Ilham, and Samed Ulucay. "Application of Genetic and Particle Swarm Optimization Algorithms to Portfolio Optimization Problem: Borsa İstanbul and Crypto Money Exchange." In 2019 4th International Conference on Computer Science and Engineering (UBMK). IEEE, 2019. http://dx.doi.org/10.1109/ubmk.2019.8907225.

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Ramzanpour, Mohammadreza, Mohammad Hosseini-Farid, Mariusz Ziejewski, and Ghodrat Karami. "Particle Swarm Optimization Method for Hyperelastic Characterization of Soft Tissues." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11829.

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Abstract Hyperelastic constitutive models such as Ogden and Mooney-Rivlin are commonly used for nonlinear characterization of soft materials and especially biomaterials such as brain tissue. The parameters of these models are usually found by curve-fitting to the experimental or in some cases, the numerical data. Most of the times, common non-linear least square curve fitting method known as Levenberg-Marquardt (LM) is employed for this purpose. In this paper, we show that the result of this method is highly dependent to the initial guesses. In some cases, the approximated curve-fitting soluti

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Engelbrecht, Andries. "Particle Swarm Optimization." In GECCO '15: Genetic and Evolutionary Computation Conference. ACM, 2015. http://dx.doi.org/10.1145/2739482.2756564.

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Engelbrecht, Andries. "Particle swarm optimization." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605342.

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Engelbrecht, AP, and CW Cleghorn. "Particle swarm optimization." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3207877.

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Li, Xiaodong, and Andries P. Engelbrecht. "Particle swarm optimization." In the 2007 GECCO conference companion. ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274118.

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Venter, Gerhard, and Jaroslaw Sobieszczanski-Sobieski. "Particle Swarm Optimization." In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1235.

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Joon-Woo Lee and Ju-Jang Lee. "Gaussian-distributed Particle Swarm Optimization: A novel Gaussian Particle Swarm Optimization." In 2013 IEEE International Conference on Industrial Technology (ICIT 2013). IEEE, 2013. http://dx.doi.org/10.1109/icit.2013.6505830.

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TURKLER, Levent, L. Ozlem AKKAN, and Taner AKKAN. "Particle Swarm Optimization in Swarm Robotics." In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2020. http://dx.doi.org/10.1109/hora49412.2020.9152861.

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Reports on the topic "Money Particle Swarm Optimization"

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Vtipil, Sharon, and John G. Warner. Earth Observing Satellite Orbit Design Via Particle Swarm Optimization. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada625084.

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Sonugür, Güray, Celal Onur Gçkçe, Yavuz Bahadır Koca, and Şevket Semih Inci. Particle Swarm Optimization Based Optimal PID Controller for Quadcopters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.12.11.

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Gökçe, Barış, Yavuz Bahadır Koca, Yılmaz Aslan, and Celal Onur Gökçe. Particle Swarm Optimization-based Optimal PID Control of an Agricultural Mobile Robot. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.04.12.

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Davis, Jeremy, Amy Bednar, and Christopher Goodin. Optimizing maximally stable extremal regions (MSER) parameters using the particle swarm optimization algorithm. Engineer Research and Development Center (U.S.), 2019. http://dx.doi.org/10.21079/11681/34160.

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Styling Parameter Optimization of the Type C Recreational Vehicle Air Drag. SAE International, 2021. http://dx.doi.org/10.4271/2021-01-5094.

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Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the R

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RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.6.

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The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with perform

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