Relevant bibliographies by topics / Particle Swarm Optimization (PSO)

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

Academic literature on the topic 'Particle Swarm Optimization (PSO)'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Particle Swarm Optimization (PSO)"

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Gonsalves, Tad, and Akira Egashira. "Parallel Swarms Oriented Particle Swarm Optimization." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/756719.

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The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO.
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Borowska, Bożena. "Learning Competitive Swarm Optimization." Entropy 24, no. 2 (February 16, 2022): 283. http://dx.doi.org/10.3390/e24020283.

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Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.
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Shen, Yuanxia, Linna Wei, Chuanhua Zeng, and Jian Chen. "Particle Swarm Optimization with Double Learning Patterns." Computational Intelligence and Neuroscience 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/6510303.

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Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.
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Aziz, Nor Azlina Ab, Zuwairie Ibrahim, Marizan Mubin, Sophan Wahyudi Nawawi, and Nor Hidayati Abdul Aziz. "Transitional Particle Swarm Optimization." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1611. http://dx.doi.org/10.11591/ijece.v7i3.pp1611-1619.

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A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs.
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Sousa-Ferreira, Ivo, and Duarte Sousa. "A review of velocity-type PSO variants." Journal of Algorithms & Computational Technology 11, no. 1 (September 18, 2016): 23–30. http://dx.doi.org/10.1177/1748301816665021.

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This paper presents a review of the particular variants of particle swarm optimization, based on the velocity-type class. The original particle swarm optimization algorithm was developed as an unconstrained optimization technique, which lacks a model that is able to handle constrained optimization problems. The particle swarm optimization and its inapplicability in constrained optimization problems are solved using the dynamic-objective constraint-handling method. The dynamic-objective constraint-handling method is originally developed for two variants of the basic particle swarm optimization, namely restricted velocity particle swarm optimization and self-adaptive velocity particle swarm optimization. Also on the subject velocity-type class, a review of three other variants is given, specifically: (1) vertical particle swarm optimization; (2) velocity limited particle swarm optimization; and (3) particle swarm optimization with scape velocity. These velocity-type particle swarm optimization variants all have in common a velocity parameter which determines the direction/movements of the particles.
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Fan, Shu-Kai S., and Chih-Hung Jen. "An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization." Mathematics 7, no. 4 (April 17, 2019): 357. http://dx.doi.org/10.3390/math7040357.

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Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.
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Ma, Zi Rui. "Particle Swarm Optimization Based on Multiobjective Optimization." Applied Mechanics and Materials 263-266 (December 2012): 2146–49. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2146.

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PSO will population each individual as the search space without a volume and quality of particle. These particles in the search space at a certain speed flight, the speed according to its own flight experience and the entire population of flight experience dynamic adjustment. We describe the standard PSO, multi-objective optimization and MOPSO. The main focus of this thesis is several PSO algorithms which are introduced in detail and studied. MOPSO algorithm introduced adaptive grid mechanism of the external population, not only to groups of particle on variation, but also to the value scope of the particles and variation, and the variation scale and population evolution algebra in proportion.
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Xu, Yu Fa, Jie Gao, Guo Chu Chen, and Jin Shou Yu. "Quantum Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.106.

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Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.
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Lenin, K. "CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 6 (June 30, 2018): 226–37. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1369.

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In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the distance between the particle and the adjacent particle, and it reflects the congestion degree of no dominated solutions. In the population, the larger the crowding distance, the sparser and more uniform. In the feasible solution space, we uniformly and randomly initialize the particle swarms and select the no dominated solution particles consisting of the elite set. After that by the methods of congestion degree choosing (the congestion degree can make the particles distribution more sparse) and the dynamic e infeasibility dominating the constraints, we remove the no dominated particles in the elite set. Then, the objectives can be approximated. Proposed crowding distance based Particle Swarm Optimization (CDPSO) algorithm has been tested in standard IEEE 30 bus test system and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and static voltage stability margin has been enhanced.
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YEN, GARY G., and MOAYED DANESHYARI. "DIVERSITY-BASED INFORMATION EXCHANGE AMONG MULTIPLE SWARMS IN PARTICLE SWARM OPTIMIZATION." International Journal of Computational Intelligence and Applications 07, no. 01 (March 2008): 57–75. http://dx.doi.org/10.1142/s1469026808002144.

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This paper proposes a method to exchange information among multiple swarms in particle swarm optimization (PSO) to facilitate evolutionary search. The algorithm is developed to solve problems having landscapes with a large number of local optima. Each swarm maintains two sets of particles; one set includes the particles to be shared with other swarms, while the other involves the particles to be replaced by individuals from other swarms. The proposed algorithm also provides a new design to search for neighboring swarms in order to share common interests among the swarm's neighborhood. The particle's movement is according to one variation of PSO with three basic terms, each one to lead the particles toward the best particle in the swarm, in the neighborhood, and in the whole population. Demonstrated through a suite of benchmark test functions, the proposed algorithm shows competitive performance with improved convergence speed.
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Dissertations / Theses on the topic "Particle Swarm Optimization (PSO)"

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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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Cleghorn, Christopher Wesley. "A Generalized theoretical deterministic particle swarm model." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33333.

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Particle swarm optimization (PSO) is a well known population-based search algorithm, originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized in a variety of application domains, providing a wealth of empirical evidence for its effectiveness as an optimizer. The PSO itself has undergone many alterations subsequent to its inception, some of which are fundamental to the PSO's core behavior, others have been more application specific. The fundamental alterations to the PSO have to a large extent been a result of theoretical analysis of the PSO's particle's long term trajectory. The most obvious example, is the need for velocity clamping in the original PSO. While there were empirical fndings that suggested that each particle's velocity was increasing at a rapid rate, it was only once a solid theoretical study was performed that the reason for the velocity explosion was understood. There has been a large amount of theoretical research done on the PSO, both for the deterministic model, and more recently for the stochastic model. This thesis presents an extension to the theoretical deterministic PSO model. Under the extended model, conditions for particle convergence to a point are derived. At present all theoretical PSO research is done under the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO's behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. The model used in this thesis greatly weakens the stagnation assumption, by instead assuming that each particle's personal best and neighborhood best can occupy an arbitrarily large number of unique positions. Empirical results are presented to support the theoretical fndings.
Dissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
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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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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
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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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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 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
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Leonard, Barend Jacobus. "Critical analysis of angle modulated particle swarm optimisers." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/61548.

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This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.
Dissertation (MSc)--University of Pretoria, 2017.
Computer Science
MSc
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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.

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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated image databases, excessive success has been done using multiclass support vector machines for image classification. Image classification is the main challenge to detect medical diagnosis. The existing work used CNN with a transfer learning mechanism that can give a solution by transferring information from GENETIC object recognition tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization PSO+MSVM method has worked with irregularities in the CXR images database by studying its group distances using a group or class mechanism. At the initial phase of the process, a median filter is used for the noise reduction from the image. Edge detection is an essential step in the process of COVID-19 detection. The canny edge detector is implemented for the detection of edges in the chest x-ray images. The PCA (Principal Component Analysis) method is implemented for the feature extraction phase. There are multiple features extracted through PCA and the essential features are optimized by an optimization technique known as swarm optimization is used for feature optimization. For the detection of COVID-19 through CXR images, a hybrid multi-class support vector machine technique is implemented. The PSO (particle swarm optimization) technique is used for feature optimization. The comparative analysis of various existing techniques is also depicted in this work. The proposed system has achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN. The proposed system is compared with existing systems and achieved better performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
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Barla-Szabo, Daniel. "A study of gradient based particle swarm optimisers." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/29927.

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Gradient-based optimisers are a natural way to solve optimisation problems, and have long been used for their efficacy in exploiting the search space. Particle swarm optimisers (PSOs), when using reasonable algorithm parameters, are considered to have good exploration characteristics. This thesis proposes a specific way of constructing hybrid gradient PSOs. Heterogeneous, hybrid gradient PSOs are constructed by allowing the gradient algorithm to optimise local best particles, while the PSO algorithm governs the behaviour of the rest of the swarm. This approach allows the distinct algorithms to concentrate on performing the separate tasks of exploration and exploitation. Two new PSOs, the Gradient Descent PSO, which combines the Gradient Descent and PSO algorithms, and the LeapFrog PSO, which combines the LeapFrog and PSO algorithms, are introduced. The GDPSO represents arguably the simplest hybrid gradient PSO possible, while the LeapFrog PSO incorporates the more sophisticated LFOP1(b) algorithm, exhibiting a heuristic algorithm design and dynamic time step adjustment mechanism. The strong tendency of these hybrids to prematurely converge is examined, and it is shown that by modifying algorithm parameters and delaying the introduction of gradient information, it is possible to retain strong exploration capabilities of the original PSO algorithm while also benefiting from the exploitation of the gradient algorithms.
Dissertation (MSc)--University of Pretoria, 2010.
Computer Science
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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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Books on the topic "Particle Swarm Optimization (PSO)"

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Lazinica, Aleksandar. Particle swarm optimization. Rijek, Crotia: InTech, 2009.

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

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

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

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

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

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

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

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

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

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Book chapters on the topic "Particle Swarm Optimization (PSO)"

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Wang, Feng-Sheng, and Li-Hsunan Chen. "Particle Swarm Optimization (PSO)." In Encyclopedia of Systems Biology, 1649–50. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_416.

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Badar, Altaf Q. H. "Different Applications of PSO." In Applying Particle Swarm Optimization, 191–208. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_11.

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Cuevas, Erik, and Alma Rodríguez. "Particle Swarm Optimization (PSO) Algorithm." In Metaheuristic Computation with MATLAB®, 159–81. First edition. | Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003006312-6.

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Fernández-Brillet, Lucas, Oscar Álvarez, and Juan Luis Fernández-Martínez. "The PSO Family: Application to the Portfolio Optimization Problem." In Applying Particle Swarm Optimization, 111–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_7.

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Yarat, Serhat, Sibel Senan, and Zeynep Orman. "A Comparative Study on PSO with Other Metaheuristic Methods." In Applying Particle Swarm Optimization, 49–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_4.

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Couceiro, Micael, and Pedram Ghamisi. "Fractional-Order Darwinian PSO." In Fractional Order Darwinian Particle Swarm Optimization, 11–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19635-0_2.

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Gkaidatzis, Paschalis A., Aggelos S. Bouhouras, and Dimitris P. Labridis. "Application of PSO in Distribution Power Systems: Operation and Planning Optimization." In Applying Particle Swarm Optimization, 321–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_17.

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Mohammed, Omar Hazem, and Mohammed Kharrich. "An Overview of the Performance of PSO Algorithm in Renewable Energy Systems." In Applying Particle Swarm Optimization, 307–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_16.

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Ehteram, Mohammad, Akram Seifi, and Fatemeh Barzegari Banadkooki. "Structure of Particle Swarm Optimization (PSO)." In Application of Machine Learning Models in Agricultural and Meteorological Sciences, 23–32. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9733-4_2.

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Mohammadazadeh, Ardahir, Mohammad Hosein Sabzalian, Oscar Castillo, Rathinasamy Sakthivel, Fayez F. M. El-Sousy, and Saleh Mobayen. "Neural Network Training Based Particle Swarm Optimization (PSO)." In Synthesis Lectures on Intelligent Technologies, 61–68. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14571-1_6.

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Conference papers on the topic "Particle Swarm Optimization (PSO)"

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Kalivarapu, Vijay K., and Eliot H. Winer. "Parallel Implementation of Particle Swarm Optimization (PSO) Through Digital Pheromone Sharing." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49444.

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In this paper, a parallelization model for PSO through sharing of digital pheromones between multiple particle swarms to search n-dimensional design spaces is presented. Digital pheromones are models simulating real pheromones produced by insects for communication to indicate a source of food or a nesting location. Particle swarms search the design space with digital pheromones aiding communication within the swarm to improve search efficiency. Digital pheromones have demonstrated the capability of searching design spaces within PSO in the previous work by authors in both single and coarse granular parallel computing environments. Multiple swarms are simultaneously deployed across various processors in the coarse granular scheme and synchronization is carried out only when all swarms achieved convergence. This was done in an effort to reduce processor-to-processor communication and network latencies. With an appropriate parallelization scheme, the benefits of digital pheromones and swarm communication can potentially outweigh the network latencies resulting in improved search efficiency and accuracy. A swarm is deployed in the design space across different processors to explore this idea. Each part of the swarm is made to communicate with each other through an additional processor. Digital pheromones aiding within a swarm, communication between swarms is facilitated through the developed parallelization model. In this paper, the development and implementation of this method together with benchmarking test cases are presented.
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Wu, Di, and G. Gary Wang. "Enhanced Particle Swarm Optimization via Reinforcement Learning." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22519.

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Abstract Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function by a random number generated from a well-selected normal distribution. The mean and variance of the normal distribution are estimated from the current state of each individual through a policy net. The historic behavior of the swarm group is learned to update the policy net and guide the selection of parameters of the normal distribution. The proposed algorithm is tested with numerical test functions and the results show that the convergence rate of PSO can be improved with the proposed Reinforcement Learning method (RL-PSO).
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Dong, Guang, and John Cooper. "Particle Swarm Optimization With Crossover and Mutation Operators Using the Diversity Criteria." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13593.

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Particle Swarm Optimization is a population based globalized search algorithm that mimics the behavior of swarms. It belongs to the larger class of evolutionary algorithms as widely used stochastic technique in the global optimization field. Since the PSO is population based, it requires no auxiliary information, such as the gradient of the problem. It is known that each particle in the PSO uses only two pieces of information, called the personal best position and the global best position, to update its moving velocity and position by generations. One disadvantage of this algorithm is that it can be easily trapped into some local optimal solutions because of the premature convergence. This may be an issue when solving complex multi-modal functions with multiple local minimums. Hence, the global optimization algorithm should have the ability to prevent being trapped into local optima by keeping wide search space and maintaining the population diversity. In order to improve the performance of the PSO for complex global optimization problems, this paper introduces both crossover and mutation operators to the basic PSO algorithm. The proposed algorithm uses the mechanism that all the particles in the current iteration will have crossover and mutation operations if the diversity criteria of the particles is reduced to be smaller than a predefined limit value. Therefore, the PSO using both crossover and mutation operators can maintain the diversity of population and enhance the search ability as to get better results while solving complex problems. This study adopts the average distance around the swarm center as the diversity measure, and extends the distance metrics to both L1 norm distance and L∞ norm distance. To verify the usability and effectiveness of the proposed algorithm, it is applied to 12 widely used nonlinear benchmark functions. These examples show that the proposed PSO with crossover and mutation operators using the diversity criteria has better optimization performance than the basic PSO by maintaining the swarm diversity. Moreover, the PSO using the L1 norm distance diversity gives better results than both L2 and L∞ norm distance for most cases.
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Das, M. Taylan, L. Canan Dulger, and G. Sena Das. "Robotic applications with Particle Swarm Optimization (PSO)." In 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2013. http://dx.doi.org/10.1109/codit.2013.6689537.

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Liu, Zhenyi, Sagar Deshpande, and Qing Hui. "Quantized Particle Swarm Optimization: An Improved Algorithm Based on Group Effect and Its Convergence Analysis." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6055.

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In this article a new simple-structure variation of Particle Swarm Optimization (PSO) algorithm is proposed. Since the standard PSO has a very good performance, the new variation retains many properties of standard PSO such as stochasticity and some other properties similar to those in Evolutionary Algorithms. However, unlike the standard PSO algorithm, in the new algorithm the particles can not only communicate with each other via the objective function but, via a new variable named “quantizer”, and hence, the new algorithm is labeled as the Quantized Particle Swarm Optimization algorithm. In addition, extensive simulations are given to show the advantages of the new algorithm over the standard PSO. Finally, the convergence analysis for deterministic version of the new algorithm is also presented.
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Kumar, Ajitabh. "Joint Optimization of Well Placement and Control Using Multi-Stage, Multi-Swarm PSO." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22045-ms.

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Abstract Evolutionary optimization algorithms, including particle swarm optimization (PSO), have been successfully applied in oil industry for production planning and control. Such optimization studies are quite challenging due to large number of decision variables, production scenarios, and subsurface uncertainties. In this work, multi-stage, multi-swarm PSO (MS2PSO) algorithm is proposed to fix certain issues with canonical PSO algorithm such as premature convergence, excessive influence of global best solution, and oscillation. Multiple experiments are conducted using Olympus benchmark to compare the efficacy of algorithms. Results from canonical PSO are first compared with two PSO variations in which hyperparameters are tuned to prioritize exploration in early phase and exploitation in late phase. Firstly, linearly decreasing inertia weight (LDIW-PSO) is used to have greater weight of current particle position during initial iterations, and vice versa. Then, time-varying acceleration coefficients (TVAC-PSO) are used to have greater weight of personal best and lesser weight of global best during the initial iterations, and vice versa. Next, a two-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first stage collapse into a single swarm in the second stage. Finally, MS2PSO with multiple stages and multiple swarms is used in which swarms recursively collapse after each stage. Multiple swarm strategy ensures that diversity is retained within the population and multiple modes are explored. Staging ensures that local optima found during initial stage does not lead to premature convergence. Optimization test case comprises of 90 control variables of which 72 are well control related and 18 are well placement related. Swarm intelligence refers to global patterns emerging from simple interactions among population. Algorithmic rules at micro level lead to social interaction at meso level, which then further leads to collective behavior at macro level. It is observed that different algorithm designs have their own benefits and drawbacks. Decreasing inertia weight in LDIW-PSO enables exploration in early stages and convergence around global best in the later stages. TVAC-PSO on the other hand restricts social learning and aids exploration in the early iterations. Social learning component is increased as run progresses, and population moves towards global best. 2SPSO aids in exploring multi-modal objective space, thus preventing premature convergence to a local optima. Swarms collapse into one group in the second stage, and run finally converges towards global best. Multiple swarms and stages in MS2PSO ensure that diversity in population is maintained throughout the run which enables continuous learning, and thus mitigates premature convergence. Both 2SPSO and MS2PSO are found to be helpful for problems with high dimensions and multiple modes where greater degree of exploration is desired. Commercial cloud computing and parallel programming were used to handle high computational workload and reduce run-time from weeks to days. Coefficients of canonical PSO are tuned in LDIW-PSO and TVAC-PSO, which helps in mitigating issues like premature convergence and oscillation. Two-stage PSO (2SPSO) where multiple swarms of first stage collapse into one in second stage, and multi-stage multi-swarm PSO (MS2PSO) where swarms recursively collapse into one are proposed. These algorithms modify the social behavior at meso scale based on number of swarms, number of stages and iterations in each stage.
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Gies, D., and Y. Rahmat-Samii. "Particle swarm optimization (PSO) for reflector antenna shaping." In IEEE Antennas and Propagation Society Symposium, 2004. IEEE, 2004. http://dx.doi.org/10.1109/aps.2004.1331828.

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

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Alrasheed, M. R., C. W. de Silva, and M. S. Gadala. "A Modified Particle Swarm Optimization Scheme and Its Application in Electronic Heat Sink Design." In ASME 2007 InterPACK Conference collocated with the ASME/JSME 2007 Thermal Engineering Heat Transfer Summer Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/ipack2007-33256.

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Particle Swarm optimization (PSO) is a robust stochastic evolutionary computation technique which is based on the movement and intelligence of swarms. In this paper the PSO algorithm is modified to improve its performance in a class of design applications in heat transfer. The developed approach includes a new term called a chaotic acceleration factor (Ca) into the algorithm, which enhances its convergence rate and its accuracy. The modified PSO is empirically tested with well-known benchmark functions. Next it is applied in plate-fin design with the objective of dissipating the maximum heat generation from an electronic component by minimizing the entropy generation rate to obtain the highest heat transfer efficiency.
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Li, H., and K. Chandrashekhara. "Structural Optimization of Laminated Composite Blade Using Particle Swarm Optimization." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88313.

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Composite blades working underwater experience complicated loading conditions. Robust design of a composite blade for hydrokinetic applications should satisfy varying loading conditions and conservative failure evaluations. Blade manufacturing using composites requires extensive optimization studies in terms of composite materials, number of layers, stacking sequences, ply thickness and orientation. In the current study, particle swarm optimization (PSO) technique is adopted to conduct composite lay-up optimization for the turbine blade. Layer numbers, ply thickness and ply orientations are optimized using standard PSO (SPSO) to minimize weight. Composite failure criteria are applied using finite element method to generate the most conservative blade design. Based on the blade lay-up design with minimized weight, stacking sequence of the blade lay-up was optimized to maximum safety factor of the designed blade using permutation discrete PSO (PDPSO). To improve the efficiency of the algorithm, the concepts of valid/invalid exchange, and memory checking were introduced into PDPSO. Meanwhile, another discrete PSO using partially mapped crossover (PMX) technique was used to validate the simulation results optimized by PDPSO. A final composite blade design with minimized weight and maximized load-carrying capacity was presented.
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Reports on the topic "Particle Swarm Optimization (PSO)"

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

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

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

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

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

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Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the RV’s external flow field is identified based on the simulation result. The approximation models of the vehicle roof parameters and air drag and vehicle volume are established by the response surface method (RSM). The vehicle roof parameters are optimized by multi-objective particle swarm optimization (MO-PSO). According to the comparison, the air drag is reduced by 2.89% and the vehicle volume is increased by 0.36%. For the RV, the proper geometry parameters can increase the inner space of the vehicle while reducing the air drag.
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