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Academic literature on the topic 'Particle swarm optimization techniques'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Particle swarm optimization techniques"

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Fedor, Bob, and Jeremy Straub. "A Particle Swarm Optimization Backtracking Technique Inspired by Science-Fiction Time Travel." AI 3, no. 2 (2022): 390–415. http://dx.doi.org/10.3390/ai3020024.

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Artificial intelligence techniques, such as particle swarm optimization, are used to solve problems throughout society. Optimization, in particular, seeks to identify the best possible decision within a search space. Problematically, particle swarm optimization will sometimes have particles that become trapped inside local minima, preventing them from identifying a global optimal solution. As a solution to this issue, this paper proposes a science-fiction inspired enhancement of particle swarm optimization where an impactful iteration is identified and the algorithm is rerun from this point, with a change made to the swarm. The proposed technique is tested using multiple variations on several different functions representing optimization problems and several standard test functions used to test various particle swarm optimization techniques.

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Gonsalves, Tad. "Two Diverse Swarm Intelligence Techniques for Supervised Learning." International Journal of Swarm Intelligence Research 6, no. 4 (2015): 55–66. http://dx.doi.org/10.4018/ijsir.2015100103.

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Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

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Madhumala, R. B., Harshvardhan Tiwari, and Verma C. Devaraj. "Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter." Cybernetics and Information Technologies 21, no. 1 (2021): 62–72. http://dx.doi.org/10.2478/cait-2021-0005.

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Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

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Ms., Navpreet Kaur* Ms. Rasleen Kaur. "A REVIEW PAPER ON AN ENHANCED FACE RECOGNITION SYSTEM USING CORRELATION METHOD AND ABPSO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 704–7. https://doi.org/10.5281/zenodo.55973.

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Face Recognition is one of the problems which can be handled very well using Hybrid techniques or mixed transform rather than single technique. This paper deals with using of Particle Swarm Optimization techniques for Face Recognition. Feature selection (FS) is a global optimization problem in machine learning, whichreduces the number of features, removes irrelevant, noisy and redundant data, and results inacceptable recognition accuracy. It is the most important step that affects the performance ofa pattern recognition system. This paper presents a novel feature selection algorithm basedon PCA [1] [2] Subspace using Accelerated Binary Particle Swarm Optimization. ABPSO is a computational paradigm based on the ideaof collaborative behavior inspired by the social behavior of bird flocking or fish schooling.  This paper proposes a novel method of Binary Particle Swarm Optimization called Accelerated Binary Particle Swarm Optimization (ABPSO) by intelligent acceleration of particles. Together with Image Pre-processing techniques such as Resolution Conversion, Histogram Equalization and Edge Detection, ABPSO is used for feature selection to obtain significantly reducedfeature subset and improved recognition rate. The performance of ABPSO is established by computing the recognition rate and the number of selected features on ORL database. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab software.  

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Dahunsi, Olurotimi A., Muhammed Dangor, Jimoh O. Pedro, and M. Montaz Ali. "Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 2 (2019): 393–415. http://dx.doi.org/10.1177/1461348419842676.

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Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.

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Tian, Xiaotao. "Particle Swarm Optimization Algorithm in Improved Electrical Control System." Journal of Physics: Conference Series 2066, no. 1 (2021): 012020. http://dx.doi.org/10.1088/1742-6596/2066/1/012020.

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Abstract In today’s social background where high-tech emerges endlessly, various production fields in our country have fully entered the era of mechanical automation and electrical automation, and electrical control systems have been widely used in our country’s electrical appliance manufacturing industry. This paper is based on the theoretical analysis of the particle swarm optimization algorithm. Based on this optimization algorithm, a brand-new particle swarm optimization algorithm is obtained. It is applied to the electrical control system to improve it and makes full use of the improved particle swarm optimization algorithm. The existing electrical control system is optimized. This article firstly analyzes the types of common electrical control systems, puts forward some basic methods to improve the control system, and then explains the effective techniques for improvement, hoping to make reference to the improvement of electrical control systems later in this article. This article first improves the particle swarm optimization algorithm, adding the ability to adjust the control system and dynamic learning factors, focusing on strengthening the later stage of the optimization of the particle swarm algorithm and the ability to converge to improve the efficiency of the calculation. The second is to improve the traditional particle swarm optimization algorithm and update the calculation method of the formula to reduce the possibility of selecting undesirable particles and affecting the optimization results. Finally, through MATLAB and reverse simulation analysis, compared with the traditional electrical control system algorithm, the improved particle swarm optimization algorithm has a faster convergence speed and high control system efficiency. The experimental research results show that the particle swarm optimization algorithm proposed in this paper has a huge advantage compared with other algorithms, and its parameter optimization gives full play to the powerful performance of the electrical control system.

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Eshan, Karunarathne, Pasupuleti Jagadeesh, Ekanayake Janaka, and Almeida Dilini. "Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 16–23. https://doi.org/10.11591/ijeecs.v20.i1.pp16-23.

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With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, “comprehensive learning particle swarm optimization (CLPSO)” to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles’ historical best information and learning probability value are used to update a particle’s velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO.

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Farmani, Mohammad Reza, Jafar Roshanian, Meisam Babaie, and Parviz M. Zadeh. "Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 226, no. 9 (2012): 2281–95. http://dx.doi.org/10.1177/0954406211432981.

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This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

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Bentley, Phillip M., and Ken H. Andersen. "Optimization of focusing neutronic devices using artificial intelligence techniques." Journal of Applied Crystallography 42, no. 2 (2009): 217–24. http://dx.doi.org/10.1107/s0021889809003483.

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The successful use is reported of a particle-swarm optimization algorithm to design a focusing, multi-channel neutron guide for the measurement of millimetre- and sub-millimetre-sized samples. For a 5 Å incident neutron wavelength on an IN5-type instrument, this results in a ninefold gain in the peak neutron count rate, and around an eightfold average gain in the count rate over the crucial 3–6 Å wavelength range, averaged over a 2 × 2 mm sample. A particle swarm method and a genetic algorithm were compared for simple neutron flux maximization, and the particle swarm was found to be faster for these kinds of problems. The focusing device was then designed by coupling the particle swarm algorithm to a full Monte Carlo neutron ray-tracing system. This realizes the `holy grail' of autonomous, self-optimizing virtual neutron devices based on life processes. The end result is superior to the manual (human) design of a focusing guide, and the design can be entirely re-optimized within a few days if the design requirements for a specific instrument should change.

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Prahara, Murinto, and Erik Ujianto. "Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 65–77. http://dx.doi.org/10.15849/ijasca.221128.05.

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Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value. Keywords: grayscale image, inertia weight, image segmentation, particle swarm optimization.

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Dissertations / Theses on the topic "Particle swarm optimization techniques"

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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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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.

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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.

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Sheehan, Shane P. "Spacecraft Trajectory Optimization Suite (STOPS): Optimization of Low-Thrust Interplanetary Spacecraft Trajectories Using Modern Optimization Techniques." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1901.

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The work presented here is a continuation of Spacecraft Trajectory Optimization Suite (STOpS), a master’s thesis written by Timothy Fitzgerald at California Polytechnic State University, San Luis Obispo. Low-thrust spacecraft engines are becoming much more common due to their high efficiency, especially for interplanetary trajectories. The version of STOpS presented here optimizes low-thrust trajectories using the Island Model Paradigm with three stochastic evolutionary algorithms: the genetic algorithm, differential evolution, and particle swarm optimization. While the algorithms used here were designed for the original STOpS, they were modified for this work. The low-thrust STOpS was successfully validated with two trajectory problems and their known near-optimal solutions. The first verification case was a constant-thrust, variable-time Earth orbit to Mars orbit transfer where the thrust was 3.787 Newtons and the time was approximately 195 days. The second verification case was a variable-thrust, constant-time Earth orbit to Mercury orbit transfer with the thrust coming from a solar electric propulsion model equation and the time being 355 days. Low-thrust STOpS found similar near-optimal solutions in each case. The final result of this work is a versatile MATLAB tool for optimizing low-thrust interplanetary trajectories.

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Ural, Mustafa. "Solution Of The Antenna Placement Problem By Means Of Global Optimization Techniques." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612674/index.pdf.

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In this thesis work, minimization of platform-based coupling between the antennas of two VHF radios on an aircraft platform and two HF radios on a ship platform is aimed. For this purpose<br>an optimal antenna placement, which yields minimum average coupling between the antennas over the whole frequency band of operation is determined for each platform. Two important global optimization techniques, namely Genetic Algorithm Optimization and Particle Swarm Optimization, are used in determination of these optimal antenna placements. Aircraft &amp<br>ship platforms and antennas placed on them are modeled based on their real electrical and physical properties in CST &ndash<br>MWS (Microwave Studio) simulation tool. For each platform, antenna placements and coupling results determined by two different optimization techniques and performances of these optimization techniques are compared with each other. At the end of this thesis work<br>for each platform, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage.

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Abdelrasoul, Nader. "Optimization Techniques For an Artificial Potential Fields Racing Car Controller." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6211.

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Context. Building autonomous racing car controllers is a growing field of computer science which has been receiving great attention lately. An approach named Artificial Potential Fields (APF) is used widely as a path finding and obstacle avoidance approach in robotics and vehicle motion controlling systems. The use of APF results in a collision free path, it can also be used to achieve other goals such as overtaking and maneuverability. Objectives. The aim of this thesis is to build an autonomous racing car controller that can achieve good performance in terms of speed, time, and damage level. To fulfill our aim we need to achieve optimality in the controller choices because racing requires the highest possible performance. Also, we need to build the controller using algorithms that does not result in high computational overhead. Methods. We used Particle Swarm Optimization (PSO) in combination with APF to achieve optimal car controlling. The Open Racing Car Simulator (TORCS) was used as a testbed for the proposed controller, we have conducted two experiments with different configuration each time to test the performance of our APF- PSO controller. Results. The obtained results showed that using the APF-PSO controller resulted in good performance compared to top performing controllers. Also, the results showed that the use of PSO proved to enhance the performance compared to using APF only. High performance has been proven in the solo driving and in racing competitions, with the exception of an increased level of damage, however, the level of damage was not very high and did not result in a controller shut down. Conclusions. Based on the obtained results we have concluded that the use of PSO with APF results in high performance while taking low computational cost.

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Tanwir, Sarmad. "Online Techniques for Enhancing the Diagnosis of Digital Circuits." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82736.

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The test process for semiconductor devices involves generation and application of test patterns, failure logging and diagnosis. Traditionally, most of these activities cater for all possible faults without making any assumptions about the actual defects present in the circuit. As the size of the circuits continues to increase (following the Moore's Law) the size of the test sets is also increasing exponentially. It follows that the cost of testing has already surpassed that of design and fabrication. The central idea of our work in this dissertation is that we can have substantial savings in the test cost if we bring the actual hardware under test inside the test process's various loops -- in particular: failure logging, diagnostic pattern generation and diagnosis. Our first work, which we describe in Chapter 3, applies this idea to failure logging. We modify the existing failure logging process that logs only the first few failure observations to an intelligent one that logs failures on the basis of their usefulness for diagnosis. To enable the intelligent logging, we propose some lightweight metrics that can be computed in real-time to grade the diagnosibility of the observed failures. On the basis of this grading, we select the failures to be logged dynamically according to the actual defects in the circuit under test. This means that the failures may be logged in a different manner for devices having different defects. This is in contrast with the existing method that has the same logging scheme for all failing devices. With the failing devices in the loop, we are able to optimize the failure log in accordance with every particular failing device thereby improving the quality of diagnosis subsequently. In Chapter 4, we investigate the most lightweight of these metrics for failure log optimization for the diagnosis of multiple simultaneous faults and provide the results of our experiments. Often, in spite of exploiting the entire potential of a test set, we might not be able to meet our diagnosis goals. This is because the manufacturing tests are generated to meet the fault coverage goals using as fewer tests as possible. In other words, they are optimized for `detection count' and `test time' and not for `diagnosis'. In our second work, we leverage realtime measures of diagnosibility, similar to the ones that were used for failure log optimization, to generate additional diagnostic patterns. These additional patterns help diagnose the existing failures beyond the power of existing tests. Again, since the failing device is inside the test generation loop, we obtain highly specific tests for each failing device that are optimized for its diagnosis. Using our proposed framework, we are able to diagnose devices better and faster than the state of the art industrial tools. Chapter 5 provides a detailed description of this method. Our third work extends the hardware-in-the-loop framework to the diagnosis of scan chains. In this method, we define a different metric that is applicable to scan chain diagnosis. Again, this method provides additional tests that are specific to the diagnosis of the particular scan chain defects in individual devices. We achieve two further advantages in this approach as compared to the online diagnostic pattern generator for logic diagnosis. Firstly, we do not need a known good device for generating or knowing the good response and secondly, besides the generation of additional tests, we also perform the final diagnosis online i.e. on the tester during test application. We explain this in detail in Chapter 6. In our research, we observe that feedback from a device is very useful for enhancing the quality of root-cause investigations of the failures in its logic and test circuitry i.e. the scan chains. This leads to the question whether some primitive signals from the devices can be indicative of the fault coverage of the applied tests. In other words, can we estimate the fault coverage without the costly activities of fault modeling and simulation? By conducting further research into this problem, we found that the entropy measurements at the circuit outputs do indeed have a high correlation with the fault coverage and can also be used to estimate it with a good accuracy. We find that these predictions are accurate not only for random tests but also for the high coverage ATPG generated tests. We present the details of our fourth contribution in Chapter 7. This work is of significant importance because it suggests that high coverage tests can be learned by continuously applying random test patterns to the hardware and using the measured entropy as a reward function. We believe that this lays down a foundation for further research into gate-level sequential test generation, which is currently intractable for industrial scale circuits with the existing techniques.<br>Ph. D.

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Storer, Jeremy J. "Computational Intelligence and Data Mining Techniques Using the Fire Data Set." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796.

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Hoori, Ammar O. "MULTI-COLUMN NEURAL NETWORKS AND SPARSE CODING NOVEL TECHNIQUES IN MACHINE LEARNING." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5743.

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Accurate and fast machine learning (ML) algorithms are highly vital in artificial intelligence (AI) applications. In complex dataset problems, traditional ML methods such as radial basis function neural network (RBFN), sparse coding (SC) using dictionary learning, and particle swarm optimization (PSO) provide trivial results, large structure, slow training, and/or slow testing. This dissertation introduces four novel ML techniques: the multi-column RBFN network (MCRN), the projected dictionary learning algorithm (PDL) and the multi-column adaptive and non-adaptive particle swarm optimization techniques (MC-APSO and MC-PSO). These novel techniques provide efficient alternatives for traditional ML techniques. Compared to traditional ML techniques, the novel ML techniques demonstrate more accurate results, faster training and testing timing, and parallelized structured solutions. MCRN deploys small RBFNs in a parallel structure to speed up both training and testing. Each RBFN is trained with a subset of the dataset and the overall structure provides results that are more accurate. PDL introduces a conceptual dictionary learning method in updating the dictionary atoms with the reconstructed input blocks. This method improves the sparsity of extracted features and hence, the image denoising results. MC-PSO and MC-APSO provide fast and more accurate alternatives to the PSO and APSO slow evolutionary techniques. MC-PSO and MC-APSO use multi-column parallelized RBFN structure to improve results and speed with a wide range of classification dataset problems. The novel techniques are trained and tested using benchmark dataset problems and the results are compared with the state-of-the-art counterpart techniques to evaluate their performance. Novel techniques’ results show superiority over techniques in accuracy and speed in most of the experimental results, which make them good alternatives in solving difficult ML problems.

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Kingry, Nathaniel. "Heuristic Optimization and Sensing Techniques for Mission Planning of Solar-Powered Unmanned Ground Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523874767812408.

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Abdussalam, Fathi M. A. "Antenna design using optimization techniques over various computaional electromagnetics. Antenna design structures using genetic algorithm, Particle Swarm and Firefly algorithms optimization methods applied on several electromagnetics numerical solutions and applications including antenna measurements and comparisons." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17217.

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Dealing with the electromagnetic issue might bring a sort of discontinuous and nondifferentiable regions. Thus, it is of great interest to implement an appropriate optimisation approach, which can preserve the computational resources and come up with a global optimum. While not being trapped in local optima, as well as the feasibility to overcome some other matters such as nonlinear and phenomena of discontinuous with a large number of variables. Problems such as lengthy computation time, constraints put forward for antenna requirements and demand for large computer memory, are very common in the analysis due to the increased interests in tackling high-scale, more complex and higher-dimensional problems. On the other side, demands for even more accurate results always expand constantly. In the context of this statement, it is very important to find out how the recently developed optimization roles can contribute to the solution of the aforementioned problems. Thereafter, the key goals of this work are to model, study and design low profile antennas for wireless and mobile communications applications using optimization process over a computational electromagnetics numerical solution. The numerical solution method could be performed over one or hybrid methods subjective to the design antenna requirements and its environment. Firstly, the thesis presents the design and modelling concept of small uni-planer Ultra- Wideband antenna. The fitness functions and the geometrical antenna elements required for such design are considered. Two antennas are designed, implemented and measured. The computed and measured outcomes are found in reasonable agreement. Secondly, the work is also addressed on how the resonance modes of microstrip patches could be performed using the method of Moments. Results have been shown on how the modes could be adjusted using MoM. Finally, the design implications of balanced structure for mobile handsets covering LTE standards 698-748 MHz and 2500-2690 MHz are explored through using firefly algorithm method. The optimised balanced antenna exhibits reasonable matching performance including near-omnidirectional radiations over the dual desirable operating bands with reduced EMF, which leads to a great immunity improvement towards the hand-held.<br>General Secretariat of Education and Scientific Research Libya

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Books on the topic "Particle swarm optimization techniques"

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

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Lazinica, Aleksandar. Particle swarm optimization. InTech, 2009.

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

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

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

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

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

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

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

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

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Book chapters on the topic "Particle swarm optimization techniques"

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Mirjalili, Seyedali, and Jin Song Dong. "Multi-objective Particle Swarm Optimization." In Multi-Objective Optimization using Artificial Intelligence Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24835-2_3.

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Kaushik, Deepika, and Mohammad Nadeem. "Portfolio optimization using batch enabled particle swarm optimization." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003635680-15.

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Kiranyaz, Serkan, Turker Ince, and Moncef Gabbouj. "Optimization Techniques: An Overview." In Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37846-1_2.

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de Jesus, Maria Aparecida, Vania V. Estrela, Osamu Saotome, and Dalmo Stutz. "Super-Resolution via Particle Swarm Optimization Variants." In Biologically Rationalized Computing Techniques For Image Processing Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61316-1_14.

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Kadavy, Tomas, Michal Pluhacek, Adam Viktorin, and Roman Senkerik. "Multi-swarm Optimization Algorithm Based on Firefly and Particle Swarm Optimization Techniques." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91253-0_38.

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Anuradha, J., and B. K. Tripathy. "Uncertainty Based Hybrid Particle Swarm Optimization Techniques and Their Applications." In Multi-objective Swarm Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46309-3_6.

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Onwubolu, Godfrey C., and Anuraganand Sharma. "Particle Swarm Optimization for the Assignment of Facilities to Locations." In New Optimization Techniques in Engineering. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39930-8_23.

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Clerc, Maurice. "Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem." In New Optimization Techniques in Engineering. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39930-8_8.

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Perera, Ricardo, and Sheng-En Fang. "Multi-objective Damage Identification Using Particle Swarm Optimization Techniques." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05165-4_8.

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Das, Dibyasundar, Suryakant Prusty, Biswajit Swain, and Tushar Sharma. "Evaluation of Optimal Feature Transformation Using Particle Swarm Optimization." In Biologically Inspired Techniques in Many Criteria Decision Making. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8739-6_19.

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Conference papers on the topic "Particle swarm optimization techniques"

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Sharma, Deepali, Mehul Manu, Anita Choudhary, Depinder Kaur, and Naveen Naval. "Optimal Design of Particle Swarm Optimization in Agricultural Based System." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877600.

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Deshmukh, Jotiram K., Ritesh Tirole, and Anand Bhaskar. "Optimization of PID Controllers for Cascade Control Loops Using Particle Swarm Optimization Techniques." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714712.

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Battu, Neelakanteshwar Rao, Ramesh Babu Veligatla, Venkatesh Arikanti, Gurunath Swargam, B. Sumanth, and Sai Nikhil SRP. "Economic Load Dispatch Using Quantum Particle Swarm Optimization Technique." In 2024 2nd International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC). IEEE, 2024. https://doi.org/10.1109/icmacc62921.2024.10894018.

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Shen, Xiaobing, Jiaze Kong, Yu Zuo, and Wilmar Martinez. "Multi-Objective Optimal Design of a DC Inductor Using Particle Swarm Optimization Techniques." In 2024 IEEE Design Methodologies Conference (DMC). IEEE, 2024. https://doi.org/10.1109/dmc62632.2024.10812124.

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Meng, Zhiqi. "Application Techniques of Particle Swarm Optimization." In 2010 International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA 2010). IEEE, 2010. http://dx.doi.org/10.1109/bwcca.2010.138.

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Srivastava, Roopak, Akshit Budhraja, and Pyari Mohan Pradhan. "An adaptive approach to swarm surveillance using particle swarm optimization." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755420.

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Cristian, Dan, Constantin Barbulescu, Stefan Kilyeni, and Vasile Popescu. "Particle swarm optimization techniques. Power systems applications." In 2013 6th International Conference on Human System Interactions (HSI). IEEE, 2013. http://dx.doi.org/10.1109/hsi.2013.6577841.

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Asl, Laleh Badri, and Vahid Ma Nezhad. "Speech Enhancement Using Particle Swarm Optimization Techniques." In 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmtma.2010.510.

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Kolakaluri, Sudhakar, Shaik Silar Nagura, Rajib Kar, S. P. Ghoshal, and D. Mandal. "Optimization of Low Noise Amplifier using Particle Swarm Optimization." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755050.

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Sadati, Nasser, Majid Zamani, and Hamid Reza Feyz Mahdavian. "Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques." In IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. IEEE, 2006. http://dx.doi.org/10.1109/iecon.2006.347309.

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Reports on the topic "Particle swarm optimization techniques"

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

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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, 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, 2021. http://dx.doi.org/10.7546/crabs.2021.04.12.

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

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Noel, Erika, Giulia Mattana, and Anthony McGoron. A Comparative Analysis of Nanoparticle Sizing Techniques for Enhanced Drug Delivery Applications. Florida International University, 2025. https://doi.org/10.25148/fiuurj.3.1.5.

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Nanoparticle-based drug delivery systems hold promise for improving therapeutic efficacy and targeting precision. However, a critical challenge in their development is ensuring size stability, as particle size directly influences biodistribution, cellular uptake, and drug release profiles. This study establishes a streamlined methodology to assess nanoparticle size consistency by comparing three widely used characterization techniques: Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM), and Nanoparticle Tracking Analysis (NTA). Two types of nanoparticles were analyzed: 100 nm diameter Gold Nanoparticles (GNP) suspended in a stabilized sodium citrate buffer and Mesoporous Silica Nanoparticles (MSN), both diluted in deionized water. DLS provides particle size distribution based on light scattering intensity, TEM offers high-resolution imaging for precise structural measurements, and NTA tracks individual particles to assess size and concentration through Brownian motion. Our findings highlight the complementary strengths of each technique, with NTA emerging as the most versatile method for rapid size assessment due to its broad size range and concentration capabilities. This research establishes a reliable, reproducible protocol for nanoparticle sizing, which can be integrated into computational models to predict drug release kinetics. These results contribute to the optimization of nanoparticle formulations for enhanced drug delivery applications.

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

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Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the 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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RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.6.

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The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with performance objectives centered on starting load, maximum load, and energy dissipation. A comprehensive dataset comprising 576 samples was generated through the integration of full-scale tests and simulations, which facilitated the training of the neural network for accurate forward predictions linking physical parameters to performance outcomes. Furthermore, a PSO-based reverse design model was developed to enable effective back-calculation from desired performance outcomes to specific geometric configurations. The BP neural network exhibited high accuracy, evidenced by a fit of 0.991, and the mechanical performance of the designed products aligned with target values in over 90% of cases, with all engineering errors remaining within acceptable limits. The proposed method significantly reduces the design time to under 5 seconds, thereby vastly improving efficiency in comparison to traditional approaches. This advancement offers a rapid and reliable reference for the design of critical components in flexible protection systems.

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