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Relevant bibliographies by topics / Binary Particle Swarm Optimization

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Academic literature on the topic 'Binary Particle Swarm Optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Binary Particle Swarm Optimization"

1

Lee, Sangwook, Sangmoon Soak, Sanghoun Oh, Witold Pedrycz, and Moongu Jeon. "Modified binary particle swarm optimization." Progress in Natural Science 18, no. 9 (2008): 1161–66. http://dx.doi.org/10.1016/j.pnsc.2008.03.018.

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Fatihah, Ifa, and Soo Young Shin. "Distributed Sensor Node Localization Using a Binary Particle Swarm Optimization Algorithm." Journal of the Institute of Electronics and Information Engineers 51, no. 7 (2014): 9–17. http://dx.doi.org/10.5573/ieie.2014.51.7.009.

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Tseng, K.-Y., C.-B. Zhang, and C.-Y. Wu. "An Enhanced Binary Particle Swarm Optimization for Structural Topology Optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 10 (2010): 2271–87. http://dx.doi.org/10.1243/09544062jmes2128.

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Particle swarm optimization (PSO), a heuristic optimization method, has been successfully applied in solving many optimization problems in real-value search space. The original binary particle swarm optimization (BPSO) uses the concept of bit flipping of the binary string to convert the velocity from a real code into a binary code. However, the conversion process cannot be reversed, and it is difficult to extend this framework to solve certain discrete optimization problems. An enhanced binary particle swarm algorithm is proposed in this study based on pure binary bit-string frameworks to deal with structural topology optimization problems. Further, two enhancement strategies, stress-based strategy and pair-switched strategy, were developed to improve the performance of the proposed algorithm for topology optimization of structure. The results of experimental cases demonstrated in this study show that the proposed enhanced binary particle swarm optimization (EBPSO) with two developed strategies is an efficient population-based approach for finding the optimal design for structural topology optimization problems of minimum compliance design and minimum weight design.

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Cho, Jae-Hoon, Dae-Jong Lee, Chang-Kyu Song, and Myung-Geun Chun. "Feature Selection Method by Information Theory and Particle S warm Optimization." Journal of Korean Institute of Intelligent Systems 19, no. 2 (2009): 191–96. http://dx.doi.org/10.5391/jkiis.2009.19.2.191.

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Hamdy, A., and A. A. Mohamed. "Greedy Binary Particle Swarm Optimization for multi-Objective Constrained Next Release Problem." International Journal of Machine Learning and Computing 9, no. 5 (2019): 561–68. http://dx.doi.org/10.18178/ijmlc.2019.9.5.840.

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Chuang, Li-Yeh, Jui-Hung Tsai, and Cheng-Hong Yang. "Binary particle swarm optimization for operon prediction." Nucleic Acids Research 38, no. 12 (2010): e128-e128. http://dx.doi.org/10.1093/nar/gkq204.

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Fang, Longjie, Haoyi Zuo, Zuogang Yang, Xicheng Zhang, Jinglei Du, and Lin Pang. "Binary wavefront optimization using particle swarm algorithm." Laser Physics 28, no. 7 (2018): 076204. http://dx.doi.org/10.1088/1555-6611/aab7d9.

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Sun, Tao, and Ming-hai Xu. "A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization." Computational Intelligence and Neuroscience 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2782679.

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Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

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Guo, Sha-sha, Jie-sheng Wang, and Meng-wei Guo. "Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm." Computational Intelligence and Neuroscience 2020 (June 8, 2020): 1–21. http://dx.doi.org/10.1155/2020/6502807.

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Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

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Beheshti, Zahra, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Memetic binary particle swarm optimization for discrete optimization problems." Information Sciences 299 (April 2015): 58–84. http://dx.doi.org/10.1016/j.ins.2014.12.016.

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

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Leonard, Barend Jacobus. "Critical analysis of angle modulated particle swarm optimisers." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/61548.

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This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.<br>Dissertation (MSc)--University of Pretoria, 2017.<br>Computer Science<br>MSc<br>Unrestricted

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Puri, Prateek. "Design Validation of RTL Circuits using Binary Particle Swarm Optimization and Symbolic Execution." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/55815.

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Over the last two decades, chip design has been conducted at the register transfer (RT) Level using Hardware Descriptive Languages (HDL), such as VHDL and Verilog. The modeling at the behavioral level not only allows for better representation and understanding of the design, but also allows for encapsulation of the sub-modules as well, thus increasing productivity. Despite these benefits, validating a RTL design is not necessarily easier. Today, design validation is considered one of the most time and resource consuming aspects of hardware design. The high costs associated with late detection of bugs can be enormous. Together with stringent time to market factors, the need to guarantee the correct functionality of the design is more critical than ever. The work done in this thesis tackles the problem of RTL design validation and presents new frameworks for functional test generation. We use branch coverage as our metric to evaluate the quality of the generated test stimuli. The initial effort for test generation utilized simulation based techniques because of their scalability with design size and ease of use. However, simulation based methods work on input spaces rather than the DUT's state space and often fail to traverse very narrow search paths in large input spaces. To encounter this problem and enhance the ability of test generation framework, in the following work in this thesis, certain design semantics are statically extracted and recurrence relationships between different variables are mined. Information such as relations among variables and loops can be extremely valuable from test generation point of view. The simulation based method is hybridized with Z3 based symbolic backward execution engine with feedback among different stages. The hybridized method performs loop abstraction and is able to traverse narrow design paths without performing costly circuit analysis or explicit loop unrolling. Also structural and functional unreachable branches are identified during the process of test generation. Experimental results show that the proposed techniques are able to achieve high branch coverage on several ITC'99 benchmark circuits and their modified variants, with significant speed up and reduction in the sequence length.<br>Master of Science

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Mishra, Chetan. "Optimal Substation Coverage for Phasor Measurement Unit Installations." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/78056.

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The PMU has been found to carry great deal of value for applications in the wide area monitoring of power systems. Historically, deployment of these devices has been limited by the prohibitive cost of the device itself. Therefore, the objective of the conventional optimal PMU placement problem is to find the minimum number devices, which if carefully placed throughout the network, either maximize observability or completely observe subject to different constraints. Now due to improved technology and digital relays serving a dual use as relay & PMU, the cost of the PMU device itself is not the largest portion of the deployment cost, but rather the substation installation. In a recently completed large-scale deployment of PMUs on the EHV network, Virginia Electric & Power Company (VEPCO) has found this to be so. The assumption then becomes that if construction work is done in a substation, enough PMU devices will be placed such that everything at that substation is measured. This thesis presents a technique proposed to minimize the number of substation installations thus indirectly minimizing the synchrophasor deployment costs. Also presented is a brief history of the PMU and its applications along with the conventional Optimal PMU placement problem and the scope for expanding this work.<br>Master of Science

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Clarke, Joshua. "Optimal design of geothermal power plants." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3472.

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The optimal design of geothermal power plants across the entire spectrum of meaningful geothermal brine temperatures and climates is investigated, while accounting for vital real-world constraints that are typically ignored in the existing literature. The constrained design space of both double-flash and binary geothermal power plants is visualized, and it is seen that inclusion of real-world constraints is vital to determining the optimal feasible design of a geothermal power plant. The effect of varying condenser temperature on optimum plant performance and optimal design specifications is analyzed. It is shown that condenser temperature has a significant effect on optimal plant design as well. The optimum specific work output and corresponding optimal design of geothermal power plants across the entire range of brine temperatures and condenser temperatures is illustrated and tabulated, allowing a scientifically sound assessment of both feasibility and appropriate plant design under any set of conditions. The performance of genetic algorithms and particle swarm optimization are compared with respect to the constrained, non-linear, simulation-based optimization of a prototypical geothermal power plant, and particle swarm optimization is shown to perform significantly better than genetic algorithms. The Pareto-optimal front of specific work output and specific heat exchanger area is visualized and tabulated for binary and double-flash plants across the full range of potential geothermal brine inlet conditions and climates, allowing investigation of the specific trade-offs required between specific work output and specific heat exchanger area. In addition to the novel data, this dissertation research illustrates the development and use of a sophisticated analysis tool, based on multi-objective particle swarm optimization, for the optimal design of geothermal power plants.

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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 (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets.<br>Thesis (PhD)--University of Pretoria, 2017.<br>Computer Science<br>PhD<br>Unrestricted

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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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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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Books on the topic "Binary 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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Olsson, Andrea E. Particle swarm optimization: Theory, techniques, and applications. Nova Science Publishers, 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, 2013.

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Clerc, Maurice. Particle Swarm Optimization. ISTE Publishing Company, 2006.

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

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Book chapters on the topic "Binary Particle Swarm Optimization"

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Huang, Xiaoyu, Enqiang Lin, Yujie Ji, and Shijun Qiao. "Using Simulated Binary Crossover in Particle Swarm Optimization." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25661-5_12.

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Yang, Qingyun, Jigui Sun, Juyang Zhang, and Chunjie Wang. "A Hybrid Particle Swarm Optimization for Binary CSPs." In Computational Intelligence and Bioinformatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816102_5.

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Tran, Quang-Anh, Quan Dang Dinh, and Frank Jiang. "Binary Hybrid Particle Swarm Optimization with Wavelet Mutation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11680-8_21.

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Lee, Ying Loong, Ayman Abd El-Saleh, Jonathan Loo, and MingFei Siyau. "Performance Investigation on Binary Particle Swarm Optimization for Global Optimization." In Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18944-4_12.

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Wang, Ling, Wei Ye, Xiping Fu, and Muhammad Ilyas Menhas. "A Modified Multi-objective Binary Particle Swarm Optimization Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_6.

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Kaleeswaran, V., S. Dhamodharavadhani, and R. Rathipriya. "Multi-crop Selection Model Using Binary Particle Swarm Optimization." In Innovative Data Communication Technologies and Application. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9651-3_5.

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Gupta, Sonu Lal, Anurag Singh Baghel, and Asif Iqbal. "Big Data Classification Using Scale-Free Binary Particle Swarm Optimization." In Harmony Search and Nature Inspired Optimization Algorithms. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0761-4_109.

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Abid, Samia, Ayesha Zafar, Rabiya Khalid, et al. "Managing Energy in Smart Homes Using Binary Particle Swarm Optimization." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61566-0_18.

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Khalid, Noor Khafifah, Zuwairie Ibrahim, Tri Basuki Kurniawan, Marzuki Khalid, and Andries P. Engelbrecht. "Implementation of Binary Particle Swarm Optimization for DNA Sequence Design." In Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02481-8_64.

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Agarwal, Shikha, and Rajesh Reghunadhan. "Some Modifications in Binary Particle Swarm Optimization for Dimensionality Reduction." In Proceedings of the 18th Online World Conference on Soft Computing in Industrial Applications (WSC18). Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00612-9_18.

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Conference papers on the topic "Binary Particle Swarm Optimization"

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Gong, Tao, and Andrew L. Tuson. "Binary particle swarm optimization." In the 9th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1276986.

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Siqueira, Hugo, Elliackin Figueiredo, Mariana Macedo, et al. "Double-Swarm Binary Particle Swarm Optimization." In 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018. http://dx.doi.org/10.1109/cec.2018.8477937.

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Zhen Ji, Tao Tian, Shan He, and Zexuan Zhu. "A memory binary particle swarm optimization." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6256150.

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Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab, and Mahdi Aliyari Shoorehdeli. "A novel binary particle swarm optimization." In 2007 Mediterranean Conference on Control & Automation. IEEE, 2007. http://dx.doi.org/10.1109/med.2007.4433821.

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Mohamadeen, K. I., Rania M. Sharkawy, and M. M. Salama. "Binary cat swarm optimization versus binary particle swarm optimization for transformer health index determination." In 2014 International Conference on Engineering and Technology (ICET). IEEE, 2014. http://dx.doi.org/10.1109/icengtechnol.2014.7016812.

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Amonchanchaigul, Thavit, and Worapoj Kreesuradej. "Input Selection Using Binary Particle Swarm Optimization." In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06). IEEE, 2006. http://dx.doi.org/10.1109/cimca.2006.127.

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Yang, Lei, Caixia Yang, and Yuliu. "Particle Swarm Optimization with Simulated Binary Crossover." In 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2014. http://dx.doi.org/10.1109/isdea.2014.161.

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Luh, Guan-Chun, and Chun-Yi Lin. "A Binary Particle Swarm Optimization for Structural Topology Optimization." In 2010 Third International Joint Conference on Computational Science and Optimization. IEEE, 2010. http://dx.doi.org/10.1109/cso.2010.231.

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Lin, Geng. "Solving unconstrained binary quadratic programming using binary particle swarm optimization." In 2013 International Conference of Information Technology and Industrial Engineering. WIT Press, 2013. http://dx.doi.org/10.2495/itie130311.

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Yang, Cheng-San, Li-Yeh Chuang, Chao-Hsuan Ke, and Cheng-Hong Yang. "Boolean binary particle swarm optimization for feature selection." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631076.

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Reports on the topic "Binary 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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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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