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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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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.
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Aminbakhsh, Saman. "Hybrid Particle Swarm Optimization Algorithm For Obtaining Pareto Front Of Discrete Time-cost Trade-off Problem." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615398/index.pdf.
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In pursuance of decreasing costs, both the client and the contractor would strive to speed up the construction project. However, accelerating the project schedule will impose additional cost and might be profitable up to a certain limit. Paramount for construction management, analyses of this trade-off between duration and cost is hailed as the time-cost trade-off (TCT) optimization. Inadequacies of existing commercial software packages for such analyses tied with eminence of discretization, motivated development of different paradigms of particle swarm optimizers (PSO) for three extensions of discrete TCT problems (DTCTPs). A sole-PSO algorithm for concomitant minimization of time and cost is proposed which involves minimal adjustments to shift focus to the completion deadline problem. A hybrid model is also developed to unravel the time-cost curve extension of DCTCPs. Engaging novel principles for evaluation of cost-slopes, and pbest/gbest positions, the hybrid SAM-PSO model combines complementary strengths of overhauled versions of the Siemens Approximation Method (SAM) and the PSO algorithm. Effectiveness and efficiency of the proposed algorithms are validated employing instances derived from the literature.
Throughout computational experiments, mixed integer programming technique is implemented to introduce the optimal non-dominated fronts of two specific benchmark problems for the very first time in the literature. Another chief contribution of this thesis can be depicted as potency of SAM-PSO model in locating the entire Pareto fronts of the practiced instances, within acceptable time-frames with reasonable deviations from the optima. Possible further improvements and applications of SAM-PSO model are suggested in the conclusion.
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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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JUNQUEIRA, Caio Marco dos Santos. "Um algoritmo para alocação ótima de detectores de afundamentos de tensão." Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/479.
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Um algoritmo para alocação ótima de detectores de afundamentos de tensão (AT) é apresentado nesta dissertação. O algoritmo utiliza a Transformada Wavelet Discreta (TWD) paraa detecção de AT e o conceito de Matriz de Observabilidade Topológica (MOT) para avaliar o desempenho dos Sistemas de Distribuição de Energia Elétrica (SDEE) quando submetidos à tais distúrbios. Para resolver o problema de alocação ótima dos dispositivos detectores de AT, utilizou-se o método Binary Particle Swarm Optimi- tization (BPSO). Adicionalmente, apresenta-se uma metodologia de criação de uma base de dados para geração automática de AT. O algoritmo foi avaliado considerando-se dois sistemas: um sistema-testedo IEEE e um SDEE que simula um alimentador real da cidade de BoaVista-PB, os quais foram simulados no software Alternative Transient Pro- gram (ATP). Osresultados obtidos indicaram que o algoritmo é capaz de detectar AT em todo o sistema, fazendo o uso da instalação de detectores em poucas barras, oque indubitavelmente, reduzirá o custo do sistema de monitoramento.
An algorithm for optimal placement of voltage sags (VS) detectors is presented in this dissertation. The algorithm uses the Discrete Wavelet Transform (DWT) for VS detection and the Topological Observability Matrix (TOM) concept to evaluate the per- formance of the Electric Power Distribution Systems (EPDS) when subjected to such disturbances. In order to solve the problem of optimal placement of the VS detecting devices, the Binary Particle Swarm Optimization (BPSO) method was used. Additionally, a methodology for the creation of a database for automatic VS generation is presented. The algorithm was evaluated considering two systems: an IEEE test system and a EPDS that simulates a real feeder in Boa Vista-PB city, which were simulated in the Alternative Transient Program (ATP) software. The results indicate that the algorithm is capable of detecting VS throughout the system, using the installation of detectors in a few buses, which will undoubtedly reduce the cost of the monitoring system.
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Bernardes, Wellington Maycon Santos. "Algoritmo enxame de partículas discreto para coordenação de relés direcionais de sobrecorrente em sistemas elétricos de potência." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-14052013-094113/.
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Este trabalho propõe uma metodologia baseada em técnicas inteligentes capaz de fornecer uma coordenação otimizada de relés direcionais de sobrecorrente instalados em sistemas de energia elétrica. O problema é modelado como um caso de programação não linear inteira mista, em que os relés permitem ajustes discretizados de múltiplos de tempo e/ou múltiplos de corrente. A solução do problema de otimização correspondente é obtida através de uma metaheurística nomeada como Discrete Particle Swarm Optimization. Na literatura técnico-científica esse problema geralmente é linearizado e aplicam-se arredondamentos das variáveis discretas. Na metodologia proposta, as variáveis discretas são tratadas adequadamente para utilização na metaheurística e são apresentados os resultados que foram comparados com os obtidos pelo modelo clássico de otimização implementado no General Algebraic Modeling System (GAMS). Tendo em vista os aspectos observados, o método permite ao engenheiro de proteção ter um subsídio adicional na tarefa da coordenação dos relés direcionais de sobrecorrente, disponibilizando uma técnica eficaz e de fácil aplicabilidade ao sistema elétrico a ser protegido, independentemente da topologia e condição operacional.This work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.
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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.Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
Unrestricted
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Karlberg, Hampus. "Task Scheduling Using Discrete Particle Swarm Optimisation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287311.
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Optimising task allocation in networked systems helps in utilising available resources. When working with unstable and heterogeneous networks, task scheduling can be used to optimise task completion time, energy efficiency and system reliability. The dynamic nature of networks also means that the optimal schedule is subject to change over time. The heterogeneity and variability in network design also complicate the translation of setups from one network to another. Discrete Particle Swarm Optimisation (DPSO) is a metaheuristic that can be used to find solutions to task scheduling. This thesis will explore how DPSO can be used to optimise job scheduling in an unstable network. The purpose is to find solutions for networks like the ones used on trains. This in turn is done to facilitate trajectory planning calculations. Through the use of an artificial neural network, we estimate job scheduling costs. These costs are then used by our DPSO meta heuristic to explore a solution space of potential scheduling. The results focus on the optimisation of batch sizes in relation to network reliability and latency. We simulate a series of unstable and heterogeneous networks and compare completion time. The baseline comparison is the case where scheduling is done by evenly distributing jobs at fixed sizes. The performance of the different approaches is then analysed with regards to usability in real-life scenarios on vehicles. Our results show a noticeable increase in performance within a wide range of network set-ups. This is at the cost of long search times for the DPSO algorithm. We conclude that under the right circumstances, the method can be used to significantly speed up distributed calculations at the cost of requiring significant ahead of time calculations. We recommend future explorations into DPSO starting states to speed up convergence as well as benchmarks of real-life performance.Optimering av arbetsfördelning i nätverk kan öka användandet av tillgängliga resurser. I instabila heterogena nätverk kan schemaläggning användas för att optimera beräkningstid, energieffektivitet och systemstabilitet. Då nätverk består av sammankopplade resurser innebär det också att vad som är ett optimalt schema kan komma att ändras över tid. Bredden av nätverkskonfigurationer gör också att det kan vara svårt att överföra och applicera ett schema från en konfiguration till en annan. Diskret Particle Swarm Optimisation (DPSO) är en meta heuristisk metod som kan användas för att ta fram lösningar till schemaläggningsproblem. Den här uppsatsen kommer utforska hur DPSO kan användas för att optimera schemaläggning för instabila nätverk. Syftet är att hitta en lösning för nätverk under liknande begränsningar som de som återfinns på tåg. Detta för att i sin tur facilitera planerandet av optimala banor. Genom användandet av ett artificiellt neuralt nätverk (ANN) uppskattar vi schemaläggningskostnaden. Denna kostnad används sedan av DPSO heuristiken för att utforska en lösningsrymd med potentiella scheman. Våra resultat fokuserar på optimeringen av grupperingsstorleken av distribuerade problem i relation till robusthet och letens. Vi simulerar ett flertal instabila och heterogena nätverk och jämför deras prestanda. Utgångspunkten för jämförelsen är schemaläggning där uppgifter distribueras jämnt i bestämda gruperingsstorlekar. Prestandan analyseras sedan i relation till användbarheten i verkliga scenarion. Våra resultat visar på en signifikant ökning i prestanda inom ett brett spann av nätverkskonfigurationer. Det här är på bekostnad av långa söktider för DPSO algoritmen. Vår slutsats är att under rätt förutsättningar kan metoden användas för att snabba upp distribuerade beräkningar förutsatt att beräkningarna för schemaläggningen görs i förväg. Vi rekommenderar vidare utforskande av DPSO algoritmens parametrar för att snabba upp konvergens, samt undersökande av algoritmens prestanda i verkliga miljöer.
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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.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 multiuser interference before transmissions, provided that the DL channel to be encountered may be accurately predicted. The MUT scheme using the classic MMSE criterion is popular owing to its simplicity. However, since the BER is the ultimate system performance indicator, in this treatise we are more interested in the Minimum BER MUT (MBER-MUT) design. Unlike the MBER-MUD, the MBER-MUT design encounters a constrained nonlinear optimization problem due to the associated total transmit power constraint. Sequential Quadratic Programming (SQP) algorithms may be used to obtain the precoder’s coefficients. However, the computational complexity of the SQP based MBER-MUT solution may be excessive for high-rate systems. Hence, as an attractive design alternative, continuous-valued PSO was invoked to find the MBER-MUT’s precoder matrix in order to reduce its computational complexity. Two PSO aided MBER-MUTs were designed and explained. The first one may be referred to as a symbol-specific MBER-MUT, while the other one may be termed as the average MBER-MUT. Our simulation results showed that both of our designs achieve an improvement in comparison to conventional linear MUT schemes, while providing a reduced complexity compared to the state-of-art SQP based MBER-MUT. Later, we introduced discrete multi-valued PSO into the context of MMSE Vector Precoding (MMSEVP) to find the optimal perturbation vector. As a nonlinearMUT scheme, the VP provides an attractive BER performance. However, the computational complexity imposed during the search for optimal perturbation vector may be deemed excessive, hence it becomes necessary to find reduced-complexity algorithms while maintaining a reasonable BER performance. Lattice-Reduction-aied (LRA) VP is the most popular approach to reduce the complexity imposed. However, the LRA VP is only capable of achieving a suboptimum BER performance, although its complexity is reduced. Another drawback of LRA VP is that its complexity is fixed, which is beneficial for real-time implenebtations, but it is unable to strike a trade-off between the target BER and its required complexity. Therefore, we developed a discrete multi-valued PSO aided MMSE-VP design, which has a flexible complexity and it is capable of iteratively improving the achievable. In Chapter 5, our contributions in the field of Minimum Bit Error Rate Vector Precoding (MBER-VP) are unveiled. Zero-Forcing Vector Precoding (ZF-VP) and MMSE Vector Precoding (MMSE-VP) had already been proposed in the literature. However, to the best of our knowledge, no VP algorithm was proposed to date based on the direct minimisation of the BER. Our improved MMSE-VP design based on the MBER criterion first invokes a regularised channel inversion technique and then superimposes a discrete-valued perturbation vector for minimising the BER of the system. To further improve the system’s BER performance, an MBER-based generalised continuous-valued VP algorithm was also proposed. Assuming the knowledge of the information symbol vector and the CIR matrix, we consider the generation of the effective symbol vector to be transmitted by directly minimising the BER of the system. Our simulation results show the advantage of these two VP schemes based on the MBER criterion, especially for rank-deficient systems, where the number of BS transmit antennas is lower than the number of MSs supported. The robustness of these two designs to the CIR estimation error are also investigated. Finally, the computational complexity imposed is also quantified in this chapter. With the understanding of the BER criterion of VP schemes, we then considered a new transceiver design by combing uniform channel decomposition and MBER vector precoding, which leads to a joint transmitter and receiver design referred as the UCD-MBER-VP scheme. In our proposed UCD-MBER-VP scheme, the precoding and equalisation matrices are calculated by the UCD method, while the perturbation vector is directly chosen based on the MBER criterion. We demonstrated that the proposed algorithm outperforms the existing benchmark schemes, especially for rank-deficient systems, where the number of users supported is more than the number of transmit antennas employed. Moreover, our proposed joint design approach imposes a similar computational complexity as the existing benchmark schemes.
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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 of the optimal solutions generated by a Particle Swarm Optimization method is completed using a cost function with different weights for the time, fuel, and angle terms. Three different scenarios are presented: a single burn case, a double burn case, and a four burn case. The results are calculated using two different orbital mechanics models: an unperturbed two-body simulation and a two-body simulation with added Earth's equatorial bulge effects. It is shown that the added perturbation reduces the total event time in the optimal solutions generated. Specific weights for the cost function are recommended for further studies.Master of Science
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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 new
technique. Particle swarm optimizer (PSO), introduced by
Kennedy and Eberhart in 1995, [1] emulates flocking
behavior of birds to solve the optimization problems.In this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO.
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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 objective functions, PSO must be parallelized to use multiprocessor systems and clusters efficiently. This thesis investigates the implications of parallelizing PSO and in particular, the details of parallelization and the effects of large swarms. PSO can be expressed naturally in Google's MapReduce framework to develop a simple and robust parallel implementation that automatically includes communication, load balancing, and fault tolerance. This flexible implementation makes it easy to apply modifications to the algorithm, such as those that improve optimization of difficult objective functions and improve parallel performance. Results show that larger swarms help with both of these goals, but they are most effective if arranged into sparse topologies with lower overhead from communication. Additionally, PSO must be modified to use communication more efficiently in a large sparse swarm for objective functions where information ideally flows quickly through a large swarm. Swarm size is usually fixed at a modest number around 50, but particularly in a parallel computational environment, much larger swarms are much more effective for deceptive objective functions. Likewise, swarms much smaller than 50 are more effective for expensive but less deceptive functions. In general, swarm size should be carefully chosen using all available information about the objective function and computational environment.
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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 via the PSO process and estimates the importance distribution from all the particles generated. This ensures that particles are located in high likelihood regions while still maintaining a certain level of diversity. This sampling mechanism is then used together with the marginal particle filter. The proposed method?s superiority in performance over the conventional particle filter is then demonstrated by simulations.
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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) and a decision support system using an enhanced Particle Swarm Optimization (PSO) algorithm (HPSO-DSS). The objective for the proposed algorithm was to have a good computational performance in order to quickly generate a solution for the automatic camera placement (ACP) problem. The new algorithm benefited from different aspects of other heuristics such as hill-climbing and greedy algorithms as well as a number of new enhancements. Methods. Both CTSS and ACP-DSS were designed and constructed using the information technology (IT) research framework. A state-of-the-art evolutionary optimization method, Hybrid PSO (HPSO), implemented to solve the ACP problem, was the core of our decision support system. Results. The CTSS is evaluated by some of its potential users after employing it and later answering a conducted survey. The evaluation of CTSS confirmed an outstanding satisfactory level of the respondents. Various aspects of the HPSO algorithm were compared to two other algorithms (PSO and Genetic Algorithm), all implemented to solve our ACP problem. Conclusions. The HPSO algorithm provided an efficient mechanism to solve the ACP problem in a timely manner. The integration of ACP-DSS into CTSS might aid the surveillance designers to adequately and more easily plan and validate the design of their security systems. The quality of CTSS as well as the solutions offered by ACP-DSS were confirmed by a number of field experts.Sarmad Rohani: 004670606805 Reza Shams: 0046704030897
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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 problems (e.g., shifted, rotated, and compositional problems) or dynamic problems (e.g., the moving peak problem and the DF1 function). This is especially true for the original PSO algorithm. In order to improve the performance of PSO to solve complex problems, we present a novel algorithm, called self-learning PSO (SLPSO). In SLPSO, each particle has four different learning strategies to deal with different situations in the search space. The cooperation of the four learning strategies is implemented by an adaptive framework at the individual level, which can enable each particle to choose the optimal learning strategy according to the properties of its own local fitness landscape. This flexible learning mechanism is able to automatically balance the behavior of exploration and exploitation for each particle in the entire search space during the whole running process. Another major contribution of this work is to adapt PSO to dynamic environments, we propose an idea that applies hierarchical clustering techniques to generate multiple populations. This idea is the first attempt to solve some open issues when using multiple population methods in dynamic environments, such as, how to define the size of search region of a sub-population, how many individuals are needed in each sub-population, and how many sub-populations are needed, etc. Experimental study has shown that this idea is effective to locate and track multiple peaks in dynamic environments.
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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 discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters. Moreover, this thesis discusses a study of boundary conditions with the invisible wall technique, controlling the convergence behaviors of PSO, discrete-valued problems, multi-objective PSO, and applications of PSO. Finally, this paper presents some kinds of improved versions as well as recent progress in the development of the PSO, and the future research issues are also given.
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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, both in terms of minimizing the cost function, and in terms of run time.
26
Cleghorn, Christopher Wesley. "Particle swarm optimization : empirical and theoretical stability analysis." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/61265.
Full textAbstract:
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 often refereed to as, particle convergence.
While new PSO variants are proposed at a rapid rate, the theoretical analysis often takes substantially longer to emerge, if at all. In some situation the theoretical analysis is not performed as the mathematical models needed to actually represent the PSO variants become too complex or contain intractable subproblems. It is for this reason that a rapid means of determining approximate stability criteria that does not require complex mathematical modeling is needed. This thesis presents an empirical approach for determining the stability criteria for PSO variants. This approach is designed to provide a real world depiction of particle stability by imposing absolutely no simplifying assumption on the underlying PSO variant being investigated. This approach is utilized to identify a number of previously unknown stability criteria.
This thesis also contains novel theoretical derivations of the stability criteria for both the fully informed PSO and the unified PSO. The theoretical models are then empirically validated utilizing the aforementioned empirical approach in an assumption free context.
The thesis closes with a substantial theoretical extension of current PSO stability research. It is common practice within the existing theoretical PSO research to assume that, in the simplest case, the personal and neighborhood best positions are stagnant. However, in this thesis, stability criteria are derived under a mathematical model where by the personal best and neighborhood best positions are treated as convergent sequences of random variables. It is also proved that, in order to derive stability criteria, no weaker assumption on the behavior of the personal and neighborhood best positions can be made. The theoretical extension presented caters for a large range of PSO variants.Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
Unrestricted
27
Peng, Yi-Fan, and 彭逸帆. "Efficient Text Segmentation Using Discrete Particle Swarm Optimization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/05792208584878890825.
Full textAbstract:
碩士中華大學
資訊工程學系碩士班
99
The task of text segmentation is to divide a long text into several shorter segments, each of which shares a common topic. It has been proven that text segmentation is beneficial to several natural language processing tasks, such as information retrieval and text summarization. In this article, a novel text segmentation algorithm based on the Discrete Particle Swarm Optimization (called DPSO-SEG) is proposed. DPSO is adopted in this algorithm to find the optimal topical segments after text preprocessing. DPSO-SEG finds the optimal topical segments by minimizing the fitness value and choosing the best solution. The performance of DPSO-SEG is evaluated and compared with some well-known text segmentation algorithms. The experimental results show that when the segment size is consistent, DPSO-SEG outperforms the others in error probability. While the time complexity of DPSO-SEG is controllable through the selection of the number of iterations to be performed, it is also advantageous when the running time is a key issue of its application.
28
Hsu, Cheng-Yu, and 徐誠佑. "A Study on Particle Swarm Optimization for Discrete Optimization Problems." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/88670775055429825213.
Full textAbstract:
博士國立交通大學
工業工程與管理系所
95
Particle Swarm Optimization (PSO) is a population-based optimization algorithm, which was developed in 1995. The original PSO is used to solve continuous optimization problems. Due to solution spaces of discrete optimization problems are discrete, we have to modify the particle position representation, particle movement, and particle velocity to better suit PSO for discrete optimization problems. The contribution of this research is that we proposed several PSO designs for discrete optimization problems. The new PSO designs are better suit for discrete optimization problems, and differ from the original PSO. In this thesis, we propose three PSOs for three discrete optimization problems respectively: the multidimensional 0-1 knapsack problem (MKP), the job shop scheduling problem (JSSP) and the open shop scheduling problem (OSSP). In the PSO for MKP, the particle position is represented by binary variables, and the particle movement are based on the concept of building blocks. In the PSO for JSSP, we modified the particle position representation using preference-lists and the particle movement using a swap operator. In the PSO for OSSP, we modified the particle position representation using priorities and the particle movement using an insert operator. Furthermore, we hybridized the PSO for MKP with a local search procedure, the PSO for JSSP with tabu search (TS), and the PSO for OSSP with beam search (BS). The computational results show that our PSOs are better than other traditional metaheuristics.
29
JUI-PINLI and 李瑞彬. "Optimal model discrimination designs by discrete particle swarm optimization." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/67949338367602836637.
Full textAbstract:
碩士國立成功大學
統計學系
103
Since the experimenters might not have prior knowledge on which main effects or interactions were likely to be significant, it is important to construct a experimental design that have the capability of screening main effects and two-factor interactions. Agboto et al. (2010) proposed model discrimination criteria. But how to construct an optimal model discrimination design based on these criteria is a difficult question. In recent years, Particle Swarm Optimization has been wildly used in many aspects because of the advantages of the PSO algorithm. In our study, since the PSO algorithm is designed to solve the continuous optimization problems, we need to modify the PSO algorithm due to particular design structure. The purpose of this paper is to present the Discrete Particle Swarm Optimization algorithm to construct an optimal model discrimination design. We implement our algorithm to optimize model discrimination design under the model discrimination criterion and compare results with Agboto et al. (2010) and the coordinate-exchange algorithm. The results show that the DPSO algorithm performs well and is compatible with other algorithms.
30
Lin, Hsieh Liang, and 林咸良. "Using Particle Swarm Optimization to Solve Some Discrete NP-complete Problems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/cth438.
Full textAbstract:
碩士聖約翰科技大學
資訊工程系碩士班
101
In this thesis, we proposed a hybrid approach of particle swarm optimization (PSO) for solving discrete NP-complete problems. In recent years, PSO has been a popular topic in evolutionary algorithm area. Compare to traditional algorithms, PSO has the advantages of quick converge speed and accurate search ability. However, the fast converge speed also leads to the disadvantages of being trapped in local optimum and over relying on initial swarm. On the other hand, unlike continuous problem, the solutions of discrete problem are sparsely spread among the problem domain. It causes a difficulty for PSO while searching optimized solutions. NP-complete problems have very high time complexity. Traditional algorithms such as greedy and dynamic programming will consume lots of time and computation in order to find solutions. Therefore, evolutionary algorithms are being applied to this kind of problems. In this research, we pick two NP-complete problems as search domain. One is n-queens problem; the other is guillotine cut problem. Although, PSO’s search ability in discrete space is not as good as in continuous space, we use some techniques to compensate the defects. In the experimental results and conclusions of this thesis, we have proved that the techniques can greatly improve PSO’s performance and stability.
31
Lu, You-Lin, and 盧又麟. "A Hybrid Particle Swarm Optimization algorithm for Discrete Simulation Optimization with One Stochastic Constraint." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zypsvr.
Full textAbstract:
碩士國立清華大學
工業工程與工程管理學系所
106
This research is based on Particle Swarm Optimization (PSO) and combine with Ranking and Selection (R&S) method to solve the discrete event simulation optimization problem with single stochastic constraint. It needs to find the optimal or nearly optimal solution in finite time or budget when solving these problems. Because the constraints of problem exist stochastic, there are many feasible and infeasible solutions existing in solution space simultaneously. When dealing with such issues, researchers used heuristic algorithms to solve the problem of excessive number of solutions, and used R&S method to solve the sampling resource allocation problem in the past. However, there is still no method to consider the factors such as excessive number of plans, feasibility of plans, and sampling resource allocation simultaneously. This makes the problem with time-consuming in simulation and inefficient in solution solving process. Based on Optimal Simulation Budget Allocation for Constrained Optimization (OCBA-CO), this research proposed an Optimal Sample Allocation Strategy for Constrained Optimization (OSAS-CO) method. This method considers the variability and feasibility of solutions, and adds the concepts of Super individual and Elite group to allocate resources on key solutions for increasing the probability of selecting the best solution. Then applied to PSO to construct a hybrid algorithm. According to the characteristics of PSO, the proposed method can improve the problem of excessive number of solutions that makes simulation time consuming. According to the characteristics of OSAS-CO, our method can also evaluate the feasibility of solutions while allocate the repetitive simulations. Then it improves the problem that PSO combines OCBA-CO will consume a lot of simulation computation budgets on solutions which have similar performance, and increase the sampling resource allocation efficiency. This research used the hybrid algorithm to solve the simulation optimization problem with single stochastic constraint. This research used two different functional models and the buffer allocation problems respectively, and reduced the total simulation number by 57%, 14.4%, and 21.96% in three experiments respectively. This research proved that OSAS-CO can improve the usage efficiency of simulation computation budgets and reduce the total simulation numbers significantly.
32
Tsai, Ming-Chi, and 蔡明冀. "Using Discrete Particle Swarm Optimization and Support Vector Machine for Spam Categorization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/34908619052784162486.
Full textAbstract:
碩士樹德科技大學
資訊管理研究所
93
The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Using a classifier based on machine learning techniques to automatically filter out spam e-mails has drawn many researchers’ attention. Some text categorization methods were used for selecting commom features of spam or non-spam, and classifying spam according to these features. Unfortunately, in some cases, too many features produce low accuracy or spent users’ time. To find the features which were appropriate for spam categorization task, we present a new feature selection algorithm which uses particle swarm optimaization. Furthermore, the support vector machine is aliso used to classify e-mails. A set of systematic experiments on e-mail categorization has been conducted with some traditional feature selection methods and our approach in order to demonstrate which one can obtain the most correct classification. Experimental results reveal that our method not only produces the same or better performace with traditional ones, but also decreases the number of features.
33
Shih, Kuan-Chung, and 施貫中. "Discrete Particle Swarm Optimization for Online Store Product Placement and Inventory Management." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/946q84.
Full textAbstract:
碩士國立臺中科技大學
流通管理系碩士班
106
The popularization of the Internet has driven the rapid development of e-commerce, prompting many traditional retail shops to change their business models towards the Internet. Therefore, how to attract consumers'' attention in online stores and allow more consumers to consume are becoming one of the important profit resources. Moreover, if the online store’s product placement and inventory management can be properly managed, it can effectively increase the profit of online store operators. This research aims to propose a discrete particle swarm optimization (DPSO) method to solve the visual-attention-dependent demand (VADD) model for the product placement and inventory management problem with online store. In order to conform the VADD model, this research converts the traditional PSO into a method suitable for discrete variables. Then through the experimental analysis to compare the results of the solution method. This research result shows that the efficiency of the proposed method is better than the DGA method in terms of their average execution time. In a sensitivity analysis shows that the execution time improvement rate of the DPSO relative to the DGA was more than 50%. Also, there was no significant difference among its various factors and the execution time of the DPSO was extremely stable and solution efficiency isn''t affected by changes in model parameters. Therefore, in academic contributions, this research presents a DPSO method to solve the VADD model. In practical contributions, this method can help online store operators rapidly and dynamically make decisions on product placement and inventory management to increase the sales of goods and profits of online store.
34
Hashemi, Seyyed Ali. "Design, high-level synthesis, and discrete optimization of digital filters based on particle swarm optimization." Master's thesis, 2011. http://hdl.handle.net/10048/1955.
Full textAbstract:
This thesis is concerned with the development of a novel discrete particle swarm optimization (PSO) technique and its application to the discrete optimization of digital filter frequency response characteristics on the one hand, and the high-level synthesis of bit-parallel digital filter data-paths on the other. Two different techniques are presented for the optimization of sharp-transition band frequency response masking (FRM) digital filters, one of which is based on the conventional finite impulse-response (FIR) digital subfilters, and a new hardware-efficient approach which is based on utilizing infinite impulse-response (IIR) digital subfilters. It is shown that further hardware efficiency can be achieved by realizing the IIR interpolation subfilters by using the bilinear-LDI approach. The corresponding discrete PSO is carried out over the canonical signed digit (CSD) multiplier coefficient space for direct mapping to digital hardware considering simultaneous magnitude and group-delay frequency response characteristics. A powerful encoding scheme is developed for the high-level synthesis of digital filters based on discrete PSO, which preserves the data dependency relationships in the digital filter data-paths. In addition, a constrained discrete PSO is developed to overcome the limitations which would manifest themselves if the conventional PSO were to be used. Several examples are presented to demonstrate the application of discrete PSO to the design, high-level synthesis and optimization of digital filters.Communications
35
張哲維. "Applying Discrete Particle Swarm Optimization to Scheduling Deliveries and Services of Large-Sized Televisions." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/65453886345131450042.
Full textAbstract:
碩士國立交通大學
工業工程與管理系所
102
Scheduling of deliveries of large-sized televisions is constrained due to many aspects. For instance, delivery times, delivery vehicles, vehicle’s capacities, delivery routes, limitations with respect to the business hours and personnel abilities. Therefore, delivery of large-sized televisions is a complicated Vehicle Routing Problem (VRP). Whether it is utilizing the company vehicles or outsourcing delivery vehicles, it should be evaluated based on a profit-maximizing perspective rather than the traditional cost minimizing standpoint in order to plan out the scheduling. Therefore, with all the constraints being taken into consideration, scheduling of delivering large sized televisions is a Heterogeneous Fleet Team Orienteering Problem with Time Window (HFTOP-TW). HFTOP-TW is consisted of Team Orienteering Problem (TOP), Heterogeneous Fleet Vehicle Routing Problem (HFVRP), and time window constraint. This research starts by constructing a mathematical model to demonstrate the HFTOP-TW. Due to the fact that this problem is non-polynomial hard (NP-hard), this research applies the Discrete Particle Swarm Optimization (DPSO) for solving such a problem. This research first proposes an algorithm that can calculate initial solutions, then it utilizes greedy route improving strategy to systematically search for better solutions. This research is conducted, tested based on experiments proposed by other researchers in order to prove the effectiveness and efficiency of the proposed method. As per research result, the proposed method has 94% chance of acquiring the best solution. This shows that the proposed method is suitable for solving the scheduling deliveries and services of large-Sized televisions. On top of that, the result also proved the value of the application of the DPSO algorithm and it could be used to maximize profits within reasonable time windows.
36
Perdana, Firdaus Fanny Putera, and Firdaus Fanny Putera Perdana. "A Discrete Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Cross-Docking." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/41352055271286302823.
Full textAbstract:
碩士國立臺灣科技大學
工業管理系
101
Cross-docking is a method that gives companies opportunities to improve their service level to their customers without high monetary investment or major changes in the infrastructure. One of the popular employments of cross-docking is in perishable product companies because maintaining product quality and on time delivery is highly important for these companies. In order to successfully employ this method, it is critical for the companies to invest time and effort in process improvement. The objective is to minimize the total transportation cost without violating vehicle capacity constraints. We propose a mathematical model and develop a discrete particle swarm optimization algorithm for the problem. Computational results indicate that the proposed solution method can effectively solve the problem.
37
Yue-Sheng-Chen and 陳躍升. "Particle swarm optimization approach to the discovery of vibratory data of the discrete system." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/82268352840690698402.
Full textAbstract:
碩士中興大學
土木工程學系所
99
The safety monitoring is a necessity in the calamities precaution of the structure failure. This is usually by applying the long-term observation and record to evaluate the structural integrity. In practice, the data missing and destroying in the long-term monitoring are unavoidable. This leads the incompleteness of data collection. Incompleteness of data may deduce an unreliable result. Thus a strategy for data mining is proposed. The approach is focused on digging out the missing data using the particle swarm optimization. The feasibility of the proposed approach is tested by experimentation simulation using Monte Carlo technique. It concludes that the subject strategy for data mining is valid and good for the discovery of vibratory data of the discrete system
38
Li, Chi-Hao, and 李其澔. "Using Discrete Particle Swarm Optimization to Find Optimal Non-collapsing and Space-filling Experimental Designs." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/88927815990054632981.
Full textAbstract:
碩士國立臺灣大學
數學研究所
102
Uniformity of experimental designs is an important issue in computer experiments recent years. To reduce the cost of handling experiment, we need to find usable designs effectively and effeciently. A design with good space-filling and non-collapsing properties may help us get the most information under some specific cost. Since a lot amount of real problems require grid discretization, based on the framework of the discrete particle swarm optimization (DPSO), we try several strategies and propose several applied methods to discuss the multi-objective issue, and will illustrate it by handling experiments on several regular and irregular feasible domains by some DPSO-based algorithms.
39
Shen, Ming Lun, and 沈銘倫. "Application of Simulated Annealing and Discrete Particle Swarm Optimization Algorithm for Permutation Flow-Shop Problems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/y786jr.
Full textAbstract:
碩士國立臺北科技大學
工業工程與管理研究所
97
All the multi-objective scheduling problems are essentially NP-hard. It usually cost us much time to solve these problems. We try to apply Hybrid discrete particle swarm optimization (HDPSO) proposed to deal with multi-objective production efficient scheduling problems. For the optimization problems, meta-heuristics have driven to be more efficiency and deal with different NP-hard problems, such as vehicle routing, production scheduling, facility layout, etc. We consider criterions which are minmize makespan and total flow time, we try to apply HDPSO where Discrete particle swarm optimization changed insides configuration to aim at scheduling problem and characteristics of exploration and exploitation itself. Furthermore, simulated annealing(SA) have escaped Discrete particle swarm optimization to trapped local optima and improvement quality solution. The proposed HDPSO was applied to well-known Taillard benchmark problems and compared with several competing meta-heuristics. Experiment result shows that we proposed HDPSO is competitive and efficient in Permutation Flow-Shop Problem(PFSP) with minmize makespan and total flow time.
40
Tjandradjaja, Evi, and 張麗珍. "An Approach using Discrete Particle Swarm Optimization and Bottleneck Heuristic to Solve Hybrid Flow Shop Scheduling Problem." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/77917822879557990517.
Full textAbstract:
碩士國立臺灣科技大學
工業管理系
98
The hybrid flow shop (HFS) is a common manufacturing environment in many industries, such as the glass, steel, paper and textile industries. In this thesis, we present a particle swarm optimization (PSO) algorithm for the HFS scheduling problem with minimum makespan objective. The main contribution of this thesis is to develop a new approach hybridizing PSO with bottleneck heuristic to fully exploit the bottleneck stage, and with simulated annealing to help escape from local optima. A restart procedure is also employed to avoid premature convergence. The proposed PSO algorithm is tested on the benchmark problems provide by Carlier and Neron. Experiment results show that the proposed algorithm outperform all the compared algorithms in solving the HFS problem.
41
Mahapatra, Ipsita Biswas. "A Novel Algorithm-Architecture Co-Designed System for Dynamic Execution-Driven Pre-Silicon Verification." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5291.
Full textAbstract:
In EDA industry, design-under-test (DUT) is a pre-silicon digital design
which is still undergoing testing phase. We perform functional-verification
of a DUT to verify whether the DUT conforms to the specifications.
Functional verification has been pre-dominantly performed through
simulation of a DUT. However, their execution speed rapidly degrades
when DUT size reaches 100 million gates. To overcome this bottleneck,
the EDA industry is increasingly adopting “hardware-accelerated
simulation platforms”, which are classified as simulation-accelerators,
emulators and FPGA prototypes. These methodologies perform functional verification
by synthesizing and implementing the DUT on a verification
platform. Hence, the existing functional-verification flows give rise to the
issue of synthesizing and implementing every new or revised DUT on the
verification platform. A functional-verification flow, “executing” the DUT
directly on the verification platform, will avoid the overhead involved in
performing hardware synthesis and implementation of every new or
revised DUT.
In our thesis, we present EX-DRIVE, an execution-driven functionalverification
system, which performs functional-verification of a DUT
without the need for hardware synthesis and implementation of the DUT,
offering significant improvement in functional-verification time. Minimal
design set-up time through adaptation of Discrete Particle Swarm
Optimization, convex and heuristic based partitioning mapping schemes, is
an added feature of the proposed system. We show that the proposed
functional-verification system achieves significant improvement in
verification performance over industry standard simulators and
emulation platforms. We have explored the design space of EXDRIVE for
various sizes of NIHC fabric and the proposed partitioning and mapping
algorithms. In order to explore the design space of EXDRIVE for the
proposed partitioning and mapping algorithms, we adopt a NIHC fabric
comprising a 3 ∗ 3 array of HC’s whereby each HC consists of a 5 ∗ 5
array of CU. Similarly, to explore the design space of EXDRIVE for
various sizes of NIHC fabric, we adopt the convex partitioning scheme
42
Liu, Bo-Fu, and 劉柏甫. "MeSwarm: Memetic Particle Swarm Optimization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/00630450672237157419.
Full textAbstract:
碩士逢甲大學
資訊工程所
93
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 many kinds of optimization problems. However, some important situations that often occur in PSO is overshooting, which is a key issue to the premature convergence and essential to the performance of PSO. Therefore, in this thesis, a variant particle swarm optimization, named memetic particle swarm optimization algorithm (MeSwarm), is proposed for tackling the overshooting problem in the movement of PSO. MeSwarm integrates the standard PSO with the Solis and Wets local search strategy to avoid the overshooting problem. Based on the recent probability of success, the Solis and Wets local search strategy can efficiently generate a new candidate solution around the current particle. Thus, an accurate moving behavior can be ensured and the overshooting problem can be prevented. In this thesis, a real-world optimization problem, flexible protein-ligand docking, and six numerical test functions optimization are used to validate the performance of MeSwarm. The experimental results indicate that MeSwarm outperforms the standard PSO and several evolutionary algorithms in terms of solution quality.
43
Ku, Wen-Yuan, and 辜文元. "Multiobjective Orthogonal Particle Swarm Optimization." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/58034016781874815034.
Full textAbstract:
碩士逢甲大學
資訊工程所
93
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 search ability. IMM can uniformly sample and analyze from the best experience of an individual particle and group particles by using a systematic reasoning method, and then efficiently generate a good candidate solution for the next move of particles. We used five benchmark functions to evaluate the performance of MOPSO, and compared MOPSO with some existing evolutionary algorithms. According to the experimental results and analysis, it is show that MOPSO performs better than some existing MOPSOs and multi-objective evolutionary algorithms.
44
Wang, An-An, and 王安安. "The Improved Particle Swarm Optimization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/22031939310812255262.
Full textAbstract:
碩士元智大學
資訊管理學系
94
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. Empirical examination of the evolution shows that the improved PSO has better efficiency than PSO.
45
Chen, Chia-Yu, and 陳珈妤. "Swiftly balanced particle swarm optimization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/22054063774671553604.
Full textAbstract:
碩士國立中央大學
電機工程研究所
99
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 be applied to many variants of PSO. In this paper, incorporating the acceleration coefficients of SBPSO and The quadratic interpolation PSO, named SBPSO-QI. In the result section, compared the proposed SBPSO and SBPSO-QI with standard PSO (SPSO), quadratic interpolation PSO (QIPSO), unified PSO (UPSO), fully informed particle swarm (FIPS), dynamic multi-swarm PSO (DMSPSO), adaptive fuzzy PSO (AFPSO), and PSO with time-varying acceleration coefficients (PSO-TVAC) across sixteen benchmark functions.
46
Schoeman, Isabella Lodewina. "Niching in particle swarm optimization." Thesis, 2010. http://hdl.handle.net/2263/26548.
Full textAbstract:
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 extend the PSO paradigm so that multiple optima can be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based, that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often requires user-specified parameters, as the sizes and placing of the niches are unknown. This thesis presents a new niching technique that uses the vector dot product of the social and cognitive direction vectors to determine niche boundaries. Thus, a separate niche radius is calculated for each niche, a process that requires minimal knowledge of the search space. This strategy differs from other techniques using niche radii where a niche radius is either required to be set in advance, or calculated from the distances between particles. The development of the algorithm is traced and tested extensively using synthetic benchmark functions. Empirical results are reported using a variety of settings. An analysis of the algorithm is presented as well as a scalability study. The performance of the algorithm is also compared to that of two other well-known PSO niching algorithms. To conclude, the vector-based PSO is extended to locate and track multiple optima in dynamic environments.Thesis (PhD)--University of Pretoria, 2010.
Computer Science
unrestricted
47
Langeveld, Joost. "Set-Based Particle Swarm Optimization." Diss., 2016. http://hdl.handle.net/2263/55834.
Full textAbstract:
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 learning. In both cases, the SBPSO is compared to three other discrete PSO algorithms from literature. On the MKP, the SBPSO is shown to outperform, with statistical significance, the other algorithms. On the FSP and using a k-nearest neighbor classifier, the SBPSO is shown to outperform, with statistical significance, the other algorithms. When a Gaussian Naive Bayes or a J48 decision tree classifier is used, no algorithm can be shown to outperform on the FSP.Dissertation (MSc)--University of Pretoria, 2016.
Computer Science
MSc
Unrestricted
48
Liao, Chen-Yi, and 廖珍怡. "Distance-Oriented Particle Swarm Optimization." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/83395179455680400584.
Full textAbstract:
碩士中原大學
資訊管理研究所
95
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) adjusts the velocity of the particle according to its distance from itself to the gbest. The second algorithm called ECPSO (Elitist-Communicative PSO) generates the better solutions to take the lead by communication between elitist and other elitists. The last algorithm called DBPSO (Distance-Based PSO) adjusts the evolve method of the particle according to its distance from itself to the gbest.
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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.
Full textAbstract:
碩士國立中央大學
電機工程研究所
100
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 smaller group will take other individuals to the optimal way. The second part is choosing not only one best as gbest but some behaving well in the flock as elitists. Thus these individuals performing well will make a greater impact than before. After improving the original particle swarm optimization, there is a water turbine governor system with PID controller which needs for parameter choosing, so we use some different particle swarm optimizations to select the parameter of the PID controller.
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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.
Full textAbstract:
碩士大同大學
機械工程學系(所)
97
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 representation are illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of structural topologies.