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

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Published: 7 June 2025

Last updated: 2 August 2025

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1

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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3

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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5

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

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6

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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7

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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8

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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10

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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Ojeda, Romero Juan Andre. "Dual Satellite Coverage using Particle Swarm Optimization." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50627.

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A dual satellite system in a Low Earth Orbit, LEO, would be beneficial to study the electromagnetic occurrences in the magnetosphere and their contributions to the development of the aurora events in the Earth's lower atmosphere. An orbit configuration is sought that would increase the total time that both satellites are inside the auroral oval. Some additional objectives include minimizing the total fuel cost and the average angle between the satellites' radius vectors. This orbit configuration is developed using a series of instantaneous burns applied at each satellite's perigee. An analysis

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12

Wilhelm, Paul Allan. "Pheromone particle swarm optimization of stochastic systems." [Ames, Iowa : Iowa State University], 2008.

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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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14

McNabb, Andrew W. "Parallel Particle Swarm Optimization and Large Swarms." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2480.

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Optimization is the search for the maximum or minimum of a given objective function. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take hours or days to optimize difficult objective functions which are deceptive or expensive. Deceptive functions may be highly multimodal and multidimensional, and PSO requires extensive exploration to avoid being trapped in local optima. Expensive functions, whose computational complexity may arise from dependence on detailed simulations or large datasets, take a long time to evaluate. For deceptive or expensive o

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Xia, Gongyi. "Particle Swarm Optimization and Particle Filter Applied to Object Tracking." Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/27610.

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The particle filter is usually used as a tracking algorithm in non-linear under the Bayesian tracking framework. However, the problems of degeneracy and impoverishment degrade its performance. The particle filter is thereafter enhanced by evolutionary optimization, in particular, Particle Swarm Optimization (PSO) is used in this thesis due to its capability of optimizing non-linear problems. In this thesis, the PSO enhanced particle filter is reviewed followed by an analysis of its drawbacks. Then, a novel sampling mechanism for the particle filter is proposed. This method generates particles

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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' 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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Li, Changhe. "Particle swarm optimization in stationary and dynamic environments." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/10284.

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Inspired by social behavior of bird flocking or fish schooling, Eberhartand Kennedy first developed the particle swarm optimization (PSO) algorithm in 1995. PSO, as a branch of evolutionary computation, has been successfully applied in many research and application areas in the past several years, e.g., global optimization, artificial neural network training, and fuzzy system control, etc… Especially, for global optimization, PSO has shown its superior advantages and effectiveness. Although PSO is an effective tool for global optimization problems, it shows weakness while solving complex probl

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Talukder, Satyobroto. "Mathematicle Modelling and Applications of Particle Swarm Optimization." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2671.

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Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discuss

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19

Endo, Makoto. "Wind Turbine Airfoil Optimization by Particle Swarm Method." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1285774101.

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Shahadat, Sharif. "Improving a Particle Swarm Optimization-based Clustering Method." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2357.

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This thesis discusses clustering related works with emphasis on Particle Swarm Optimization (PSO) principles. Specifically, we review in detail the PSO clustering algorithm proposed by Van Der Merwe & Engelbrecht, the particle swarm clustering (PSC) algorithm proposed by Cohen & de Castro, Szabo’s modified PSC (mPSC), and Georgieva & Engelbrecht’s Cooperative-Multi-Population PSO (CMPSO). In this thesis, an improvement over Van Der Merwe & Engelbrecht’s PSO clustering has been proposed and tested for standard datasets. The improvements observed in those experiments vary from slight to moderate

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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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22

Czogalla, Jens [Verfasser]. "Particle Swarm Optimization for Scheduling Problems / Jens Czogalla." Aachen : Shaker, 2010. http://d-nb.info/1122546289/34.

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23

Sieve, Carlo <1990&gt. "Particle Swarm Optimization per la selezione del portafoglio." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6817.

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HANSRAJ and BIJESH YADAV. "PARTICLE SWARM OPTIMIZATION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20425.

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An optimisation algorithm based on the behaviors of social organisms is known as particle swarm optimizatio (PSO).It represents a set of potential answers to an optimi sation issue as a swarm of moving particles in the parameter space. The performance of the particles is guided by their own performance and the performance of their neighbors, leading to an optimized solution. This thesis presents a study of the impact of boundary conditions on the performance of Particle Swarm Optimization (PSO) through the use of the invisible wall technique. The convergence behaviors of PSO are analyzed

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Liu, Bo-Fu, and 劉柏甫. "MeSwarm: Memetic Particle Swarm Optimization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/00630450672237157419.

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碩士<br>逢甲大學<br>資訊工程所<br>93<br>Many scientific, engineering and biological problems involve the optimization of a set of parameter. These problems include examples like minimizing the affinity between the moleculars in the process of the drug design by finding the suitable conformation of the led compound, or training a human model for predicting the behavior of human. Numerical optimization algorithms have been proposed to solve these problems, with varying degrees of success. The Particle Swarm Optimization (PSO) is relatively new technique that has been empirically shown to perform well on ma

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Ku, Wen-Yuan, and 辜文元. "Multiobjective Orthogonal Particle Swarm Optimization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/58034016781874815034.

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碩士<br>逢甲大學<br>資訊工程所<br>93<br>This paper proposes a novel multiobjective orthogonal particle swarm optimization algorithm MOPSO using a novel intelligent move mechanism IMM to solve multiobjective optimization problems. High performance of MOPSO mainly arises from two parts: one is using generalized Pareto-based scale-independent scoring function (GPSISF) can efficiently assign all candidate solutions a discriminate score, and then decide candidate solutions level. The other one is to replace the conventional move behavior of PSO with IMM based on orthogonal experimental design to enhance the s

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Wang, An-An, and 王安安. "The Improved Particle Swarm Optimization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/22031939310812255262.

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碩士<br>元智大學<br>資訊管理學系<br>94<br>This paper presents an improved particle swarm optimization which improved the efficiency on the multimodal optimization problems. The new algorithm has two stages: In the first stage, we split the problem’s search space into k sub-space, and then using k particle swarms to find the optimum in each sub-space, the local optimum in the original search space. During this stage, particles can move to different swarms. In the second stage, we organize the several local optimums finding in the first stage into a new swarm, and continue searching for the global optimum.

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Chen, Chia-Yu, and 陳珈妤. "Swiftly balanced particle swarm optimization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/22054063774671553604.

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碩士<br>國立中央大學<br>電機工程研究所<br>99<br>Swiftly balanced particle swarm optimization (SBPSO) is a new variant of particle swarm optimization which can quickly balanced the personal and social experience. A new strategy of the acceleration coefficients makes SBPSO more effective, because the swarm can efficiently adjust the velocity by changing the acceleration coefficients. The acceleration coefficients of SBPSO are obtained by three segment line dependent on the swarm convergence. The advantage is that SBPSO become more accurate and also easy to implement. The acceleration coefficients of SBPSO can

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Schoeman, Isabella Lodewina. "Niching in particle swarm optimization." Thesis, 2010. http://hdl.handle.net/2263/26548.

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Optimization forms an intrinsic part of the design and implementation of modern systems, such as industrial systems, communication networks, and the configuration of electric or electronic components. Population-based single-solution optimization algorithms such as Particle Swarm Optimization (PSO) have been shown to perform well when a number of optimal or suboptimal solutions exist. However, some problems require algorithms that locate all or most of these optimal and suboptimal solutions. Such algorithms are known as niching or speciation algorithms. Several techniques have been proposed to

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Langeveld, Joost. "Set-Based Particle Swarm Optimization." Diss., 2016. http://hdl.handle.net/2263/55834.

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Particle swarm optimization (PSO) algorithms have been successfully applied to discrete-valued optimization problems. However, in many cases the algorithms have been tailored specifically for the problem at hand. This study proposes a generic set-based particle swarm optimization algorithm, called SBPSO, for use on discrete-valued optimization problems that can be formulated as set-based problems. The performance of the SBPSO is then evaluated on two different discrete optimization problems: the multidimensional knapsack problem (MKP) and the feature selection problem (FSP) from machine learni

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Liao, Chen-Yi, and 廖珍怡. "Distance-Oriented Particle Swarm Optimization." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/83395179455680400584.

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碩士<br>中原大學<br>資訊管理研究所<br>95<br>Particle Swarm Optimization (PSO) is a stochastic, population-based evolutionary search technique proposed by Kennedy and Eberhart in 1995, which is inspired by flocks of birds and shoals of fish. It is popular due to its simplicity in its implementation, as a few parameters are needed to be tuned. PSO has difficulties in controlling the balance between exploration and exploitation. In order to improve the performance of PSO and maintain the diversity of particles, we proposed three improved algorithm, the first algorithm called VAPSO (Velocity-Adjustable PSO) ad

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Chen, Hong-Yi, and 陳弘毅. "Yare immigration particle swarm optimization." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/62031048999599345757.

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碩士<br>國立中央大學<br>電機工程研究所<br>100<br>The yare immigration particle swarm optimization (YIPSO) is an improved method of the standard particle swarm optimization by observing behaviors of the flocks of fishes, birds and students to enhancing the performance of the swarm. There are usually a few smaller groups in the flock because of the ability, interest, individuality, etc., and these groups might affect the result of the flock. Considering thess situations, two concept are added to PSO as YIPSO. The first one is dividing the flock into smaller groups randomly, therefore the best one of each sm

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Lin, Yu-shu, and 林玉書. "Structural topology optimization using particle swarm optimization algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/64821925349729675667.

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碩士<br>大同大學<br>機械工程學系(所)<br>97<br>The particle swarm optimization (PSO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the social behavior of birds flocking, is applied to problems of continuum structural topology design. An overview of the PSO and binary PSO algorithms are first described. A discretized topology design representation and the method for mapping binary particle into this representation are then detailed. Subsequently, modified binary PSO algorithm and logic binary PSO algorithm adopt the concept of genotype-phenotype r

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Freire, Hélio Alves. "Many-objective optimization with particle swarm optimization algorithm." Doctoral thesis, 2017. http://hdl.handle.net/10348/7424.

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Tese de Doutoramento em Engenharia Eletrotécnica e de Computadores<br>Muitos dos problemas de optimização envolvem diversos objectivos sujeitos a algumas restrições e que devem ser considerados simultaneamente. Ao contrário dos problemas uni-objectivo em que se procura a solução óptima global, a resolução dos problemas com múltiplos objectivos dão origem a um conjunto de soluções, chamado frente de Pareto. Nas últimas duas décadas os algoritmos evolutivos conjuntamente com o princípio da dominância de Pareto têm demonstrado uma grande capacidade para obter um conjunto de soluções próximas da f

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Yin, Shu-Chen, and 殷淑貞. "Particle Swarm Optimization for Dynamic Clustering." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/34944364571653171729.

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碩士<br>大同大學<br>資訊經營學系(所)<br>96<br>Data clustering, one of the major research technologies of data mining, is the process of grouping together similar multi-dimensional data vectors into a number of clusters. The process of data clustering needs to consider the number of clusters and the result of clusters. The natural number of clusters will influence the final clustering result. How to find the optimal number of clusters becomes an important issue. In this research, the author develops a novel dynamic clustering method, called Particle Swarm Optimization for Dynamic Clustering (PSODC), to clus

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Jhang, Bo-Yong, and 張伯墉. "Adaptive Self-Learning Particle Swarm Optimization." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/53088840990478946129.

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碩士<br>國立中央大學<br>電機工程學系<br>104<br>This thesis proposes a new particle swarm optimization (PSO) called Adaptive Self-Learning Particle Swarm Optimization (ASLPSO), and applies it to the classification problem. A self-learning method is introduced in the ASLPSO that every particle randomly selects its learning object among the better particles to acquire useful information. We also designs a dynamic transition strategy to improve the searching approach of particles during the iterations. In the experiments, the performance of the proposed ASLPSO is compared to several improved PSO’s in the litera

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Lee, Chia-Yi, and 李家宜. "Gaze Tracking with Particle Swarm Optimization." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/24049735188194612938.

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碩士<br>國立臺灣師範大學<br>電機工程學系<br>104<br>The eye tracking system is a new human machine interface device which can analyze the gaze path by tracking the eyeball movement. It has become increasingly popular in the consumer market, due to the fact that the recorded gaze tracking results can be applied to the study of human attention span, cognitive psychology and in the fields of neuroscience, psychology, education, as well as consumer products. However, gaze tracking system mostly rely on infra-ray (IR) light to enhance the image quality of the eyeball, making the application environments and scenari

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Kuo, I.-Hong, and 郭奕宏. "Particle Swarm Optimization and Its Applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/46331665410594829569.

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博士<br>國立臺灣科技大學<br>電機工程系<br>97<br>In this thesis we present several hybrid particle swarm optimization algorithms to solve the traveling salesman problem, the flow-shop scheduling problem and the forecasting problems respectively. The experimental results show that the proposed algorithms are very efficient and effective. The objective of the traveling salesman problem is to find a shortest tour that starts from a city, visits every city once, and finally comes back to the start city. A hybrid swarm intelligence algorithm consists of the random-key search method, the individual enhancement sche

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SIAN, ZENG BO, and 曾柏憲. "Switching Self-Learning Particle Swarm Optimization." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/kp94b6.

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碩士<br>國立中央大學<br>電機工程學系<br>105<br>In this thesis, we propose a new particle swarm algorithm called Switching Self-Learning Particle Swarm Optimization (SSLPSO), which switches to different velocity updating formulas in different stages(periods), so the amount of calculation can be minimized. By adding "the rise and fall functions", the convergence rate can be faster. While the diversity of the particles are abundent at the beginning, the particles apply self-learning method at the later stage to learn from those who have the best performance, thus not falling into local optimum but reaching the

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PANDEY, GANESH KUMAR. "LOAD FLOW USING PARTICLE SWARM OPTIMIZATION." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14645.

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ABSTRACT Load flow (LF) is an important tool in the planning and operation of power systems. It is usually solved using conventional numerical techniques like Newton-Raphson (NR) and Gauss-Siedel (GS). Most of these techniques depend on getting the inverse of the Jacobian matrix of the system. Such techniques fail to solve the load flow in some conditions, like Heavy loaded system, Ill-conditioned Jacobian matrix. In this thesis an application of particle swarm optimization (PSO) in solving the load flow problem as an optimization problem is discussed. A MATLAB program has been develop

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Shen, Zhe-Ping, and 沈哲平. "Optimization of Slope Stability Analysis using Particle Swarm Optimization." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/m9j65g.

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博士<br>國立臺北科技大學<br>工程科技研究所<br>102<br>This study used a 3-D laser scanner to scan landslides at Houshanyue in the Wenshan District of Taipei city. Using STABL and Particle Swarm Optimization (PSO) to find the critical slip surfaces of the slope under study. First, the slope was scanned to generate point cloud data, which in terms were used to create the Digital Elevation Model (DEM). Then, the slope analysis was optimized by PSO in order to calculate the lowest Factor of Safety (FS). In this study, the DEM was analyzed in many 2-D profiles of different orientations in order to find the most crit

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Wu, Pei-Rong, and 吳佩蓉. "Fuel Cell Optimization Parameters Estimate by Particle Swarm Optimization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/05414094875121472401.

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碩士<br>逢甲大學<br>資訊電機工程碩士在職專班<br>99<br>This thesis to propose the concept of environmental protection enhancing, countries in the world actively developing of green renewable energy to reduce carbon dioxide concentration. The characteristics of intermittent renewable energy sources required to ensure effective energy storage system, to achieve higher economic efficiency. Due to fuel cell is contain storage function as well as energy converters, that make fuel cell development greatly improved. The thesis will not only introduce some swarm intelligence of the algorithm and description, but also

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Chen, Yu-Cheng, and 陳昱丞. "Particle Swarm Optimization for Sloving Clustering Problem." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03949079499092328741.

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碩士<br>中原大學<br>資訊管理研究所<br>96<br>Data Clustering in Data Mining is the common and important technology. It can find out the data distribution and meaning in the huge data.By the Swarm Intelligence rising, more researchers use this technology of the Swarm Intelligence on data clustering, also get better effect.However,in these researches, Particle Swarm Optimization Algorithm(PSO) also has good effect on data clustering. PSO is a population-based stochastic search process, modeled after the social behavior of a bird flock, has the character of robust、quick converges and easy accomplish,and in spa

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Wu, Wun-Ci, and 吳文棋. "Jump Improved Multi-Objective Particle Swarm Optimization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/3tn3ub.

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碩士<br>國立東華大學<br>電機工程學系<br>96<br>Abstract Recently, genetic algorithm (GA) and particle swarm optimization (PSO) are the mainstream in the research of multi-objective problem. Although they are belongs to population-based evolutionary algorithms, GA’s Individuals evolve by mating and mutation which exhibited the feature of jumping, PSO’s individuals evolve by the past experience which presented the feature of mobility. GA has wide searching ability of avoiding the local search and can increase the probability of finding the global best, but it converges slower and spends more time to generate n

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Hsieh, Tsung-Ta, and 謝宗達. "Writing CAI Software On Particle Swarm Optimization." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/t964r8.

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碩士<br>大同大學<br>資訊工程學系(所)<br>102<br>Many universities have started teaching relevant courses on particle swarm optimization algorithm(PSO) in recent years due to its excellent results and high usability. However, users find it rather inconvenient during the course of learning and researching without the aid of teaching software. This paper proposes a CAI software with graphical user interface on PSO that allows users to study and research PSO conveniently. The graphical user interface not only helps to teach but also raise enthusiasm for learning as users have a better understanding of the PSO t

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Chiu, Hung-Chih, and 邱鴻志. "Improvements and Applications of Particle Swarm Optimization." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/36903935725561800180.

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碩士<br>國立中央大學<br>電機工程研究所<br>98<br>In this thesis, in order to enhance each variable particle’s searching ability and efficiency, a fuzzy logic control is implemented to adapt the acceleration parameters of particle swarm optimization algorithm (PSO). The important condition of fully utilizing the particle swarm optimization algorithm is to keep advance between extensive searching and exploring global optimal. This method has two advantages. One is that it is flexible to integrate with other PSO techniques to enhance the searching performance further. The other is that it is only used three simp

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Ji, Zih-Ming, and 紀梓民. "Exploration of improvement of particle swarm optimization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/95426045149514135294.

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碩士<br>國立中興大學<br>機械工程學系所<br>96<br>The particle swarm optimization (PSO) method has good performance and is easy to be programmed. Since it uses multiple particle to search the optimum solution, it has the better chance to find the global solution. Althogh it has those advantages mentioned, it consumes a lot of computation time to compute the fitnesses of particles and some parameters in PSOmay affect the solution significantly. According to this understanding, this thesis tries to modify PSO algorithm in order to improve its quality of solutions. The main approches include: using uniform design

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Hsu, Chih-Chiang, and 許志強. "Fuzzy Particle Swarm Optimization for Data Clustering." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/25242692505059030762.

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碩士<br>大同大學<br>資訊經營學系(所)<br>96<br>This paper proposes a new data clustering algorithm which is based on fuzzy techniques and particle swarm optimization (PSO). As pointed out by some researchers: the standard PSO always converges very quickly towards the optimal positions but may slow its convergence speed when it is reaching a minimum [9]. This paper is trying to solve this problem by integrating a Fuzzy technique with PSO to allow each particle to update its new velocity and next position according to the current position of other better particles, in addition to gbest, pbest and itself. Not

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Wang, Liang-Chi, and 王良吉. "Classification Rule Discovery with Particle Swarm Optimization." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/94012661225821276618.

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碩士<br>國立高雄第一科技大學<br>資訊管理所<br>95<br>Particle Swarm Optimization (PSO) is a new optimization technique in the artificial intelligence field. Since 1995, it has been gradually applied to the field of optimization and data mining. In this paper, we applied the discrete PSO with the Pittsburgh approach to build a PSO-based classifier. We also propose the concept of the rule mask and combine the rule deletion operator and the minimum description length-based fitness function to make the particle’s length variably. In this paper, we also apply the USD algorithm to make the classifier deal with the d

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Tseng, Chao-Tang, and 曾兆堂. "Particle Swarm Optimization for Solving Scheduling Problems." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/06557163854213788055.

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博士<br>國立臺灣科技大學<br>企業管理系<br>94<br>Particle Swarm Optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. In resent years, the continuous and discrete versions of PSO have been developed to solve continuous optimization problems. The applications of PSO to scheduling problems are extremely few. In this dissertation, we focus on developing the efficient PSO algorithms based on the continuous or discrete versions of PSO to solve the scheduling problems. To explore the potential applications of PSO, we consider three scheduling problems with different complexities wh

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