Relevant bibliographies by topics / PSO (PRATICLE SWARM OPTIMIZATION)

Contents

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

Academic literature on the topic 'PSO (PRATICLE SWARM OPTIMIZATION)'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "PSO (PRATICLE SWARM OPTIMIZATION)"

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Aziz, Nor Azlina Ab, Zuwairie Ibrahim, Marizan Mubin, Sophan Wahyudi Nawawi, and Nor Hidayati Abdul Aziz. "Transitional Particle Swarm Optimization." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (2017): 1611. http://dx.doi.org/10.11591/ijece.v7i3.pp1611-1619.

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A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a num
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Golubovic, Ruzica, and Dragan Olcan. "Antenna optimization using Particle Swarm Optimization algorithm." Journal of Automatic Control 16, no. 1 (2006): 21–24. http://dx.doi.org/10.2298/jac0601021g.

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We present the results for two different antenna optimization problems that are found using the Particle Swarm Optimization (PSO) algorithm. The first problem is finding the maximal forward gain of a Yagi antenna. The second problem is finding the optimal feeding of a broadside antenna array. The optimization problems have 6 and 20 optimization variables, respectively. The preferred values of the parameters of the PSO algorithm are found for presented problems. The results show that the preferred parameters of PSO are somewhat different for optimization problems with different number of dimens
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Jiang, Chang Yuan, Shu Guang Zhao, Li Zheng Guo, and Chuan Ji. "An Improved Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 195-196 (August 2012): 1060–65. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1060.

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Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.
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Shen, Yuanxia, Linna Wei, Chuanhua Zeng, and Jian Chen. "Particle Swarm Optimization with Double Learning Patterns." Computational Intelligence and Neuroscience 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/6510303.

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Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave
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Xu, Yu Fa, Jie Gao, Guo Chu Chen, and Jin Shou Yu. "Quantum Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.106.

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Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.
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Moraglio, Alberto, Cecilia Di Chio, Julian Togelius, and Riccardo Poli. "Geometric Particle Swarm Optimization." Journal of Artificial Evolution and Applications 2008 (February 21, 2008): 1–14. http://dx.doi.org/10.1155/2008/143624.

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Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new Geometric PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinat
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Zhang, Guan Yu, Xiao Ming Wang, Rui Guo, and Guo Qiang Wang. "An Improved Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 394 (September 2013): 505–8. http://dx.doi.org/10.4028/www.scientific.net/amm.394.505.

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This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.
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Hudaib, Amjad A., and Ahmad Kamel AL Hwaitat. "Movement Particle Swarm Optimization Algorithm." Modern Applied Science 12, no. 1 (2017): 148. http://dx.doi.org/10.5539/mas.v12n1p148.

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Particle Swarm Optimization (PSO) ia a will known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed a optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and
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Gonsalves, Tad, and Akira Egashira. "Parallel Swarms Oriented Particle Swarm Optimization." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/756719.

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The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, indivi
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Ma, Zi Rui. "Particle Swarm Optimization Based on Multiobjective Optimization." Applied Mechanics and Materials 263-266 (December 2012): 2146–49. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2146.

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PSO will population each individual as the search space without a volume and quality of particle. These particles in the search space at a certain speed flight, the speed according to its own flight experience and the entire population of flight experience dynamic adjustment. We describe the standard PSO, multi-objective optimization and MOPSO. The main focus of this thesis is several PSO algorithms which are introduced in detail and studied. MOPSO algorithm introduced adaptive grid mechanism of the external population, not only to groups of particle on variation, but also to the value scope o
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Dissertations / Theses on the topic "PSO (PRATICLE SWARM OPTIMIZATION)"

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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.

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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the a
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Urade, Hemlata S., and Rahila Patel. "Performance Evaluation of Dynamic Particle Swarm Optimization." IJCSN, 2012. http://hdl.handle.net/10150/283597.

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Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Unconstrained optimization problems can be formulated as a D-dimensional minimization problem as follows: Min f (x) x=[x1+x2+……..xD] where D is the number of the parameters to be optimized. subjected to: Gi(x) <=0, i=1…q Hj(x) =0, j=q+1,……m Xε [Xmin, Xmax]D, q is the number of inequality constraints and m-q is the number of equality constraints. The particle swarm optimizer (PSO) is a relatively
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Cleghorn, Christopher Wesley. "A Generalized theoretical deterministic particle swarm model." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33333.

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Particle swarm optimization (PSO) is a well known population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized in a variety of application domains, providing a wealth of empirical evidence for its effectiveness as an optimizer. The PSO itself has undergone many alterations subsequent to its inception, some of which are fundamental to the PSO's core behavior, others have been more application specific. The fundamental alterations to the PSO have to a large extent been a result of theoretical analysis of the PSO's particle's long term t
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Amiri, Mohammad Reza Shams, and Sarmad Rohani. "Automated Camera Placement using Hybrid Particle Swarm Optimization." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3326.

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Context. Automatic placement of surveillance cameras&apos; 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decision support system (ACP-DSS) integrated into a 3D CAD environment could assist the surveillance system designers with the process of finding good camera settings considering multiple constraints. Objectives. In this study we designed and implemented two subsystems: a camera toolset in SketchUp (CTSS) an
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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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Cleghorn, Christopher Wesley. "Particle swarm optimization : empirical and theoretical stability analysis." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/61265.

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Particle swarm optimization (PSO) is a well-known stochastic population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. Given PSO's success at solving numerous real world problems, a large number of PSO variants have been proposed. However, unlike the original PSO, most variants currently have little to no existing theoretical results. This lack of a theoretical underpinning makes it difficult, if not impossible, for practitioners to make informed decisions about the algorithmic setup. This thesis focuses on the criteria needed for particle stability, or as it is
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Veselý, Filip. "Aplikace optimalizační metody PSO v podnikatelství." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222445.

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This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.
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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.

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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach exec
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Lai, Chun-Hau. "Diseño e implementación de algoritmos aproximados de clustering balanceado en PSO." Tesis, Universidad de Chile, 2012. http://www.repositorio.uchile.cl/handle/2250/111954.

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Magíster en Ciencias, Mención Computación<br>Este trabajo de tesis está dedicado al diseño e implementación de algoritmos aproximados que permiten explorar las mejores soluciones para el problema de Clustering Balanceado, el cual consiste en dividir un conjunto de n puntos en k clusters tal que cada cluster tenga como m ́ınimo ⌊ n ⌋ puntos, k y éstos deben estar lo más cercano posible al centroide de cada cluster. Estudiamos los algoritmos existentes para este problema y nuestro análisis muestra que éstos podrían fallar en entregar un resultado óptimo por la ausencia de la evaluación de los re
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Oldewage, Elre Talea. "The perils of particle swarm optimization in high dimensional problem spaces." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/66233.

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Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has been applied successfully to a variety of domains. This thesis examines the behaviour of PSO when applied to high dimensional optimisation problems. Empirical experiments are used to illustrate the problems exhibited by the swarm, namely that the particles are prone to leaving the search space and never returning. This thesis does not intend to develop a new version of PSO speci cally for high dimensional problems. Instead, the thesis investigates why PSO fails in high dimensional search spaces.
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Books on the topic "PSO (PRATICLE SWARM OPTIMIZATION)"

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López, Javier. Optimización multi-objetivo. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/45214.

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Cuando hablamos de optimización en el ámbito de las ciencias de la computación hacemos referencia al mismo concepto coloquial asociado a esa palabra, la concreción de un objetivo utilizando la menor cantidad de recursos disponibles, o en una visión similar, la obtención del mejor objetivo posible utilizando todos los recursos con lo que se cuenta. Los métodos para encontrar la mejor solución (óptima) varían de acuerdo a la complejidad del problema enfrentado. Para problemas triviales, el cerebro humano posee la capacidad de resolverlos (encontrar la mejor solución) directamente, pero a medida
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Book chapters on the topic "PSO (PRATICLE SWARM OPTIMIZATION)"

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Wang, Feng-Sheng, and Li-Hsunan Chen. "Particle Swarm Optimization (PSO)." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_416.

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Badar, Altaf Q. H. "Different Applications of PSO." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_11.

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Cuevas, Erik, and Alma Rodríguez. "Particle Swarm Optimization (PSO) Algorithm." In Metaheuristic Computation with MATLAB®. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-6.

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Couceiro, Micael, and Pedram Ghamisi. "Fractional-Order Darwinian PSO." In Fractional Order Darwinian Particle Swarm Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19635-0_2.

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Ehteram, Mohammad, Akram Seifi, and Fatemeh Barzegari Banadkooki. "Structure of Particle Swarm Optimization (PSO)." In Application of Machine Learning Models in Agricultural and Meteorological Sciences. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9733-4_2.

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Kao, Yucheng, Ming-Hsien Chen, and Kai-Ming Hsieh. "Combining PSO and FCM for Dynamic Fuzzy Clustering Problems." In Swarm Intelligence Based Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12970-9_1.

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Fernández-Brillet, Lucas, Oscar Álvarez, and Juan Luis Fernández-Martínez. "The PSO Family: Application to the Portfolio Optimization Problem." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_7.

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Yarat, Serhat, Sibel Senan, and Zeynep Orman. "A Comparative Study on PSO with Other Metaheuristic Methods." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_4.

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Deroussi, Laurent. "A Hybrid PSO Applied to the Flexible Job Shop with Transport." In Swarm Intelligence Based Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12970-9_13.

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Gkaidatzis, Paschalis A., Aggelos S. Bouhouras, and Dimitris P. Labridis. "Application of PSO in Distribution Power Systems: Operation and Planning Optimization." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_17.

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Conference papers on the topic "PSO (PRATICLE SWARM OPTIMIZATION)"

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Hu, Jhen-Jai, Yu-Te Su, and Tzuu-Hseng S. Li. "A novel ecological-biological-behavior praticle swarm optimization for Ackley's function." In 2010 International Symposium on Computer, Communication, Control and Automation (3CA). IEEE, 2010. http://dx.doi.org/10.1109/3ca.2010.5533436.

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Das, M. Taylan, L. Canan Dulger, and G. Sena Das. "Robotic applications with Particle Swarm Optimization (PSO)." In 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2013. http://dx.doi.org/10.1109/codit.2013.6689537.

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Schutze, Oliver, El-ghazali Talbi, Gregorio Toscano Pulido, Carlos Coello Coello, and Luis Vicente Santana-Quintero. "A Memetic PSO Algorithm for Scalar Optimization Problems." In 2007 IEEE Swarm Intelligence Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sis.2007.368036.

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Vatankhah, Ramin, Shahram Etemadi, Mohammad Honarvar, Aria Alasty, Mehrdad Boroushaki, and Gholamreza Vossoughi. "Online velocity optimization of robotic swarm flocking using particle swarm optimization (PSO) method." In 2009 6th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2009. http://dx.doi.org/10.1109/isma.2009.5164776.

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Pappala, V. S., and I. Erlich. "Power system optimization under uncertainties: A PSO approach." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668276.

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Gies, D., and Y. Rahmat-Samii. "Particle swarm optimization (PSO) for reflector antenna shaping." In IEEE Antennas and Propagation Society Symposium, 2004. IEEE, 2004. http://dx.doi.org/10.1109/aps.2004.1331828.

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Kohler, Manoela, Leonardo Forero, Marley Vellasco, Ricardo Tanscheit, and Marco Aurelio Pacheco. "PSO+: A nonlinear constraints-handling particle swarm optimization." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7744102.

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Ahmadie, Beryl Labique, Wanda Athira Luqyana, Wayan Firdaus Mahmudy, and Rio Arifando. "Milkfish Feed Optimization Using Adaptive Particle Swarm Optimization (PSO) Algorithm." In 2019 International Conference on Sustainable Information Engineering and Technology (SIET). IEEE, 2019. http://dx.doi.org/10.1109/siet48054.2019.8986094.

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Daneshyari, Moayed, and Gary G. Yen. "Solving constrained optimization using multiple swarm cultural PSO with inter-swarm communication." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586103.

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Wu, Di, and G. Gary Wang. "Enhanced Particle Swarm Optimization via Reinforcement Learning." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22519.

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Abstract Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function by a random number generated from a well-selected normal distribution. The mean and variance of the normal distribution are estimated from the current state of each individual through a policy net. The historic behavior of
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Reports on the topic "PSO (PRATICLE SWARM OPTIMIZATION)"

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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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