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Zeitschriftenartikel zum Thema „Algoritmus PSO“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Tang, Zhonghua, and Yongquan Zhou. "A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning." Journal of Intelligent Systems 24, no. 1 (2015): 69–83. http://dx.doi.org/10.1515/jisys-2013-0066.

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AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global op

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Jovanović, Dražen, Martin Ćalasan, and Milovan Radulović. "Estimation of solar cell parameters using PSO algorithm." Tehnika 74, no. 1 (2019): 91–96. http://dx.doi.org/10.5937/tehnika1901091j.

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3

Mujičić, Danilo, Martin Ćalasan, and Milovan Radulović. "Application of PSO algorithm in transformer parameter estimation." Tehnika 74, no. 2 (2019): 251–57. http://dx.doi.org/10.5937/tehnika1902251m.

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4

Klásková, Eva, and Jan David. "Algorithm for recognition a seriously ill child." Česko-slovenská pediatrie 77, no. 5 (2022): 284–86. http://dx.doi.org/10.55095/cspediatrie2022/045.

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5

Novikova, Tatyana, Svetlana Evdokimova, and Roman Medvedev. "Computer-aided design of the location of wireless cellular base stations." Modeling of systems and processes 16, no. 4 (2023): 61–70. http://dx.doi.org/10.12737/2219-0767-2023-16-4-61-70.

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The planning of wireless base stations is a multi-purpose task of optimizing combinations, and the main goals of optimization include cost reduction, increased coverage and quality preservation. The paper pays attention to the consideration of the coverage radius, the quality of signal transmission and two types of signal interference. To implement the tasks set for the location of the base station, the following algorithms were used: PMET-PSO and PPSO-GA, which became the basis for the module for solving problems of designing the location of base stations in the proposed automated design syst

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Quah, Titus, Derek Machalek, and Kody M. Powell. "Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process." Processes 8, no. 11 (2020): 1497. http://dx.doi.org/10.3390/pr8111497.

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One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles mode

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Vojtíšek, Michal, and Martin Kotek. "Estimation of Engine Intake Air Mass Flow using a generic Speed-Density method." Journal of Middle European Construction and Design of Cars 12, no. 1 (2014): 7–15. http://dx.doi.org/10.2478/mecdc-2014-0002.

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SHRNUTÍ Měření výfukových emisí spalovacích motorů během reálného provozu přenosnými zařízeními umístěnými na palubě vozidla (PEMS) je důležitou součástí hodnocení dopadu nových paliv a technologií na životní prostředí a lidské zdraví. Znalost aktuálního toku výfukových plynů je jedním z nezbytných předpokladů pro takové provozní měření. Jedním z nejjednodušších způsobů je výpočet z toku nasáveného vzduchu, který je vypočten z měřených otáček motoru a tlaku a teploty náplně v sacím potrubí. V této práci byl obecný algoritmus využívající odhad dopravní účinnosti libovolného běžného čtyřdobého m

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DERDİMAN, Mehmet K. "RELIABILITY-BASED CROSS-SECTION OPTIMIZATION OF CANTILEVER SLABS USING DISCRETE PSO ALGORITHM." Mühendislik Bilimleri ve Tasarım Dergisi 10, no. 3 (2022): 987–99. http://dx.doi.org/10.21923/jesd.952838.

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Günümüzün gelişen bilgisayar teknolojileri ile kullanımı ve araştırması en çok artan konuların başında optimizasyon gelmektedir. Parçacık sürü optimizasyon (PSO) algoritması ise uzun yıllardır üzerinde araştırmalar yapılmış ve geçerliliği kabul görmüş popülasyon tabanlı algoritmalar arasında yer almaktadır. Konsol döşemelerde döşemenin sehim sınırlarını aşmadan TS500 taşıma gücü kriterlerini sağlayacak çok sayıda farklı çözümü mevcuttur. Bu çözümler arasından en faydalı ve ekonomik olanın seçilmesi önemlidir. Eğer optimal tasarıma ilişkin bir kriter ortaya konulursa, bu tasarımcı için önemli b

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Vilcu, Adrian, Ionut Herghiligiu, Ion Verzea, and Raluca Lazarescu. "A NEW PSO-BASED ALGORITHM FOR AN OPERATIONAL MANAGEMENT PROBLEM." International Journal of Modern Manufacturing Technologies 14, no. 3 (2022): 299–303. http://dx.doi.org/10.54684/ijmmt.2022.14.3.299.

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Operational management issues represent a permanent challenge for the current economic environment and the research activity. This research will model a Travelling Salesman Problem (TSP). The complexity of this fundamental problem (np-hard) allows a chance to apply and develop heuristic methods and evolutionary algorithms along with exact methods (dynamic programming, branch & bound). This paper proposes a new discrete algorithm to solve the TSP based on the Particle Swarm Optimization (PSO) technique. The features of this method are fast determination through an iterative process of the o

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Ali, Omer, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri, and Imran Ashraf. "Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems." Mathematics 11, no. 21 (2023): 4406. http://dx.doi.org/10.3390/math11214406.

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Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w. The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization

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Lenin, K. "POLAR PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 335–45. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1378.

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This paper presents Polar Particle Swarm optimization (PPSO) algorithm for solving optimal reactive power problem. The standard Particle Swarm Optimization (PSO) algorithm is an innovative evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. In the proposed PPSO algorithm that enhances the behaviour of PSO and avoids the local minima problem by using a polar function to search for more points in the search space in order to evaluate the efficiency of proposed algorithm, it has been tested on IEEE 30 bus system

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Sotelo-Figueroa, Marco Aurelio, Héctor José Puga Soberanes, Juan Martín Carpio, Héctor J. Fraire Huacuja, Laura Cruz Reyes, and Jorge Alberto Soria-Alcaraz. "Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/545191.

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In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two pe

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Chu, Shu-Chuan, Zhi-Gang Du, and Jeng-Shyang Pan. "Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks." Applied Sciences 10, no. 3 (2020): 930. http://dx.doi.org/10.3390/app10030930.

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The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scal

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Dynhora-Danheyda, Ramírez-Ochoa, Pérez-Domínguez Luis Asunción, and Martínez-Gómez Erwin Adán. "Comparison of PSO with the Hybrid Algorithms MOORA-PSO and DA-PSO for Decision Making." Advances in Modelling and Analysis B 66, no. 1-4 (2023): 26–30. http://dx.doi.org/10.18280/ama_b.661-404.

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15

Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and

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Li, Zhaobo, Yimin Deng, and Shuanglei Sun. "Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization." Electronics 11, no. 9 (2022): 1377. http://dx.doi.org/10.3390/electronics11091377.

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A vehicle adaptive cruise system can control the speed and the safe distance between vehicles rapidly and effectively, which is an integral part of an intelligent driver assistance system. Adaptive cruise predictive control algorithms based on variable compass operator pigeon-inspired optimization (PIO) and PSO are proposed to improve the time response characteristics of multi-objective adaptive cruise system predictive control. Firstly, a longitudinal kinematic model of an adaptive cruise system was established and linearly discretized. Secondly, the multi-objective optimal cost function and

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Dr.K., Lenin. "POLAR PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research - Granthaalayah 6, no. 6 (2018): 335–45. https://doi.org/10.5281/zenodo.1308976.

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This paper presents Polar Particle Swarm optimization (PPSO) algorithm for solving optimal reactive power problem. The standard Particle Swarm Optimization (PSO) algorithm is an innovative evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. In the proposed PPSO algorithm that enhances the behaviour of PSO and avoids the local minima problem by using a polar function to search for more points in the search space in order to evaluate the efficiency of proposed algorithm, it has been tested on IEEE 30 bus s

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Cheng, Shi, Yuhui Shi, and Quande Qin. "Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization." International Journal of Swarm Intelligence Research 2, no. 3 (2011): 43–69. http://dx.doi.org/10.4018/jsir.2011070104.

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Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm’s ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy

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Hsiao, Sung-Jung, and Wen-Tsai Sung. "Improving Particle Swarm Optimization Analysis Using Differential Models." Applied Sciences 12, no. 11 (2022): 5505. http://dx.doi.org/10.3390/app12115505.

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This paper employs the approach of the differential model to effectively improve the analysis of particle swarm optimization. This research uses a unified model to analyze four typical particle swarm optimization (PSO) algorithms. On this basis, the proposed approach further starts from the conversion between the differential equation model and the difference equation model and proposes a differential evolution PSO model. The simulation results of high-dimensional numerical optimization problems show that the algorithm’s performance can be greatly improved by increasing the step size parameter

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Buranaaudsawakul, Techatat, Kittipong Ardhan, Sitthisak Audomsi, Worawat Sa-Ngiamvibool, and Rattapon Dulyala. "Optimal turning of a 2-DOF proportional-integral-derivative controller based on a chess algorithm for load frequency control." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 146–55. https://doi.org/10.11591/ijece.v15i1.pp146-155.

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Load frequency control is necessary for power system management. Thepower system must maintain a frequency range to ensure power supplystability. System faults and demand fluctuations may cause frequencies tochange quickly. System stability and integrity suffer. We are optimizingthe two-degree-of-freedom (2-DOF) proportional-integral-derivative (PID)controllers chess algorithm. This article addresses electrical load frequencyregulation. We employ classical control theory and current adjustment. It aimsfor electrical system efficiency and dependability. It checks for errors usingintegral absolu

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Schutte, Jaco F., Byung-Il Koh, Jeffrey A. Reinbolt, Raphael T. Haftka, Alan D. George, and Benjamin J. Fregly. "Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization." Journal of Biomechanical Engineering 127, no. 3 (2005): 465–74. http://dx.doi.org/10.1115/1.1894388.

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Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently- developed version of the particle swarm optimization (PSO) algorithm to

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Sunita, Devi. "Analysis Of Different Approaches Used To Solve Travelling Salesman Problem." International Journal of Advance and Applied Research 6, no. 9 (2022): 622–27. https://doi.org/10.5281/zenodo.7189467.

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The travelling salesperson problem (TSP) is one among the globally recognized and broadly studied problems and is known to be an NP-Hard problem in the field of operational research. It is a mathematical problem where one needs to find the shortest possible route in a collection of cities by passing through each one city exactly once. In order to solve this problem in polynomial time we don’t have any well suitable algorithm till date. Even though we have a variety of algorithms so far that provide near optimal solutions. To solve this problem two broad categories of algorithms are used

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Adil, Zinah. "Performance evaluation of the particle swarm optimization for clustering based on different parameter selection." Iraqi Journal of Intelligent Computing and Informatics (IJICI) 1, no. 1 (2022): 1–10. http://dx.doi.org/10.52940/ijici.v1i1.1.

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Particle Swamp Optimization (PSO) is an effective method for solving a wide range of problems. However, the most existing PSO algorithms easily trap into local optima when solving complex multimodal function optimization problems. In this paper, we explain the importance of PSO algorithm’s general purposes to optimize strategy which has various parameters that decide its conduct and viability in advancing a given issue. This study gives a rundown of the best selections of parameters for different advancement situations which should enable the specialist to accomplish better outcomes with less

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Yun, Ruan. "Comparative Analysis of Genetic Algorithms and Particle Swarm Optimization Algorithms for Optimal Reservoir Operation." Applied Mechanics and Materials 90-93 (September 2011): 2727–33. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2727.

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Apart from traditional optimization techniques, modern heuristic optimization techniques, like genetic algorithms (GA), particle swarm optimization algorithm (PSO) have been widely used to solve optimization problems. This paper deals with comparative analysis of GA and PSO and their applications in a reservoir operation problem. Extensive component analysis, parameter sensitivity analysis of GA and PSO show that both GA and PSO can be used for optimal reservoir operation, but they display different features. GA can obtain very high approximate global optimal solutions of the problem with a hi

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Izci, Davut, and Serdar Ekinci. "Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization." International Journal of Robotics and Control Systems 3, no. 3 (2023): 599–608. http://dx.doi.org/10.31763/ijrcs.v3i3.1116.

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The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a

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Rameshkumar, K. "Extension of PSO and ACO-PSO algorithms for solving Quadratic Assignment Problems." IOP Conference Series: Materials Science and Engineering 377 (June 2018): 012192. http://dx.doi.org/10.1088/1757-899x/377/1/012192.

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Xu, Zhengyu, Guofeng Zhao, Xian Liao, and Nengyi Fu. "The PSO-IFAH optimization algorithm for transient electromagnetic inversion." PLOS ONE 20, no. 1 (2025): e0317596. https://doi.org/10.1371/journal.pone.0317596.

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As a non-contact method, the transient electromagnetic (TEM) method has the characteristics of high efficiency, small impact of device, no limitation of site range, and high resolution, and is a hot topic in current research. However, the research on the refined data processing method of TEM is lag, which seriously restricts the application in superficial engineering investigation and is a key problem that needs to be solved urgently. The particle swarm optimization (PSO) algorithm and firefly algorithm (FA) were successful swarm intelligence algorithms inspired by nature. However, the accurac

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Le, Thi Bao Tran1, Thu Nguyet Minh2 Nguyen, and Van Dong3 Tra. "Adjusting Parametersin Optimize Function PSO." International Journal of Social Science and Human Research 05, no. 03 (2022): 950–65. https://doi.org/10.5281/zenodo.6731942.

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: Particel Swarm Optimization (PSO) is a form of population evolutionary algorithm introduced in the early 1995 by two American scientists, sociologist James Kennedy and electrical engineer. Russell. This thesis mainly deals with the PSO optimization algorithm and the methods of adaptive adjustment of the parameters of the PSO optimization. The thesis also presents some basic problems of PSO, from PSO history to two basic PSO algorithms and improved PSO algorithms. Some improved PSO algorithms will be presented in the thesis, including: airspeed limit, inertial weighting, and coefficient limit

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S.Deepa, S.Kannadhasan, and M.Shanmuganantham. "Energy Consumption Techniques for Wireless Sensor Networks Using PSO and TSA Algorithm." International Journal of Engineering Research & Science 4, no. 7 (2018): 01–05. https://doi.org/10.5281/zenodo.1324100.

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<strong><em>Abstract</em></strong><strong>—</strong> <em>Optimizing energy consumption is the main concern for designing and planning the operation of the Wireless Sensor Networks (WSNs). Clustering technique is one of the methods utilized to extend lifetime of the network and balancing energy consumption among sensor nodes of the network. In this paper, we propose the recently developed, heuristic optimization algorithms like Particle Swarm Optimization (PSO) and Tabu Search Algorithm(TSA) as well as the traditional Fuzzy C-Means (FCM) clustering algorithms. A comparison is made with th

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Dharmawan, Weiskhy Steven. "KOMPARASI ALGORITMA KLASIFIKASI SVM-PSO DAN C4.5-PSO DALAM PREDIKSI PENYAKIT JANTUNG." I N F O R M A T I K A 13, no. 2 (2022): 31. http://dx.doi.org/10.36723/juri.v13i2.301.

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Buranaaudsawakul, Techatat, Kittipong Ardhan, Sitthisak Audomsi, Worawat Sa-ngiamvibool, and Rattapon Dulyala. "Optimal turning of a 2-DOF proportional-integral-derivative controller based on a chess algorithm for load frequency control." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 146. http://dx.doi.org/10.11591/ijece.v15i1.pp146-155.

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Load frequency control is necessary for power system management. The power system must maintain a frequency range to ensure power supply stability. System faults and demand fluctuations may cause frequencies to change quickly. System stability and integrity suffer. We are optimizing the two-degree-of-freedom (2-DOF) proportional-integral-derivative (PID) controllers chess algorithm. This article addresses electrical load frequency regulation. We employ classical control theory and current adjustment. It aims for electrical system efficiency and dependability. It checks for errors using integra

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Adrian, Mochammad Ilham, and Eka Angga Laksana. "Optimalisasi Parameter Support Vector Machine dengan Algoritma PSO untuk Tugas Klasifikasi Sentimen Ulasan IMDb." Jurnal Algoritma 22, no. 1 (2025): 288–99. https://doi.org/10.33364/algoritma/v.22-1.2306.

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Dalam penelitian ini, digunakan algoritma LinearSVC dari metode Support Vector Machine (SVM) untuk analisis sentimen terhadap data ulasan IMDb. Proses ekstraksi fitur dilakukan dengan TF-IDF Vectorizer. Tantangan utama terletak pada penentuan nilai hyperparameter, khususnya parameter regulasi (C), yang sangat menentukan kualitas hasil prediksi. Untuk mengatasi hal ini, penelitian ini menerapkan algoritma Particle Swarm Optimization (PSO) guna menemukan nilai C terbaik. Eksperimen menunjukkan bahwa tanpa optimasi, model SVM hanya mencapai akurasi 89,48%, tetapi setelah PSO diterapkan, nilai opt

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Xu, Yunwu, and Yan Li. "Wireless Sensor Network Coverage Optimization for Internet of Things." JUCS - Journal of Universal Computer Science 29, no. (12) (2023): 1535–53. https://doi.org/10.3897/jucs.103738.

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The objective of this work is to improve the existing Wireless Sensor Network coverage optimization method. The pigeon-inspired optimization algorithm was first evaluated, and its shortcomings were noted. The pigeon-inspired optimization method was then enhanced with the good point set, Yin-Yang optimization algorithm, and opposition-based learning. To test the improved algorithm, five representative standard functions were chosen: sphere function (f1), Rosenbrock function (f2), Levy function (f3), Schwefel function (f4), and Levy function N.13 (f5). The algorithm's speed of convergence may be

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Maulida, Nida, Nana Suarna, and Willy Prihartono. "ANALISIS ULASAN SENTIMEN APLIKASI MOBILE JKN DENGAN ALGORITMA SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 2 (2024): 1651–58. http://dx.doi.org/10.36040/jati.v8i2.9105.

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Inovasi elektronik dalam layanan jaminan kesehatan pemerintah dikenal sebagai aplikasi Mobile JKN yang memudahkan peserta Jaminan Kesehatan Nasional-Kartu Indonesia Sehat (JKN-KIS) untuk mendapatkan layanan dan informasi. Dengan inovasi ini, ada banyak pro dan kontra sehingga banyak komentar muncul di kolom review Google Play Store. Kecenderungan respon pengguna dalam menggunakan aplikasi Mobile JKN dapat diketahui dengan analisis sentimen. Analisis sentimen adalah sistem untuk mengenali dan mengekstraksi review dalam bentuk teks. Penelitian ini bertujuan untuk mengukur tingkat akurasi, presis

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M., Zaki Zakaria, Mutalib Sofianita, Abdul Rahman Shuzlina, J. Elias Shamsulpp, and Zambri Shahuddin A. "Solving RFID mobile reader path problem with optimization algorithms." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 1110–16. https://doi.org/10.11591/ijeecs.v13.i3.pp1110-1116.

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Radio Frequency Identification (RFID) is a one of the fastest growing and most beneficial technologies being adopted by businesses today. One of the important issues is localization of items in a warehouse or business premise and to keep track of the said items, it requires devices which are costly to deploy. This is because many readers need to be placed in a search space. In detecting an object, a reader will only report the signal strength of the tag detected. Once the signal strength report is obtained, the system will compute the coordinates of the RFID tags based on each data grouping. I

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Tekchandani, Prakash, and Aditya Trivedi. "Clock Drift Management Using Nature Inspired Algorithms." Journal of Information Technology Research 5, no. 4 (2012): 48–62. http://dx.doi.org/10.4018/jitr.2012100104.

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Time Synchronization is common requirement for most network applications. It is particularly essential in a Wireless Sensor Networks (WSNs) to allow collective signal processing, proper correlation of diverse measurements taken from a set of distributed sensor elements and for an efficient sharing of the communication channel. The Flooding Time Synchronization Protocol (FTSP) was developed explicitly for time synchronization of wireless sensor networks. In this paper, we optimized FTSP for clock drift management using Particle Swarm Optimization (PSO), Variant of PSO and Differential Evolution

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Ding, Jinjin, Qunjin Wang, Qian Zhang, Qiubo Ye, and Yuan Ma. "A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications." Mathematical Problems in Engineering 2019 (March 28, 2019): 1–12. http://dx.doi.org/10.1155/2019/5213759.

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This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm

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Cai, Y. X., Y. Y. Xu, T. R. Zhang, and D. D. Li. "Threshold image target segmentation technology based on intelligent algorithms." Computer Optics 44, no. 1 (2020): 137–41. http://dx.doi.org/10.18287/2412-6179-co-630.

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This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has hi

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Lin, Lianhai, Zhigang Wang, Liqin Tian, Junyi Wu, and Wenxing Wu. "A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning." PLOS ONE 19, no. 1 (2024): e0297066. http://dx.doi.org/10.1371/journal.pone.0297066.

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With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is prop

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Tiwari, Anchal, Ankit Gupta, Alok Patel, Rajeshri Lanjewar, and Phanish Kumar Sahu. "Pathfinder Pro: Implementation of Various Pathfinding Algorithms." International Journal of Research Publication and Reviews 6, no. 5 (2025): 4501–7. https://doi.org/10.55248/gengpi.6.0525.1735.

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Zain, Ali, Lal Harijan Bharat, Din Memon Tayab, Nafi Nazmus, and Memon Ubed-u-Rahman. "Digital FIR Filter Design by PSO and its variants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 6 (2021): 136–41. https://doi.org/10.35940/ijrte.F5515.039621.

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<strong>Abstract:</strong> Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great

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Rehman, Shafiqur, Salman Khan, and Luai Alhems. "The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design." FME Transactions 48, no. 4 (2020): 922–30. http://dx.doi.org/10.5937/fme2004922r.

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Wind energy has become a strong alternative to traditional sources of energy. One important decision for an efficient wind farm is the optimal layout design. This layout governs the placement of turbines in a wind farm. The inherent complexity involved in this process results in the wind farm layout design problem to be a complex optimization problem. Particle Swarm Optimization (PSO) algorithm has been effectively used in many studies to solve the wind farm layout design problem. However, the impact of an important set of PSO parameters, namely, the acceleration coefficients, has not received

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Wu, Jui-Yu. "Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches." Mathematical Problems in Engineering 2012 (2012): 1–36. http://dx.doi.org/10.1155/2012/841410.

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This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO a

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Ostojić, Andrija, Martin Ćalasan, and Saša Mujović. "Implementation of the V2G model for optimising the load curve of the power system using PSO algorithm." Tehnika 74, no. 6 (2019): 841–46. http://dx.doi.org/10.5937/tehnika1906841o.

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Duca, Anton, Laurentiu Duca, Gabriela Ciuprina, Asim Egemen Yilmaz, and Tolga Altinoz. "PSO algorithms and GPGPU technique for electromagnetic problems." International Journal of Applied Electromagnetics and Mechanics 53 (March 9, 2017): S249—S259. http://dx.doi.org/10.3233/jae-140166.

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Li, Guo He, Xiang Yue, Wei Jiang Wu, and Jiang Hui Zhao. "Method of Mathematical Modeling Based on PSO Algorithms." Applied Mechanics and Materials 347-350 (August 2013): 2447–51. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2447.

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In order to set up universal and non-linear map of variables, a full binary tree is constructed as mathematical model. Leaf nodes of the full binary tree are linear combination of input variables, and used as inputs of next nodes. On the basis of weighting two inputs by selector for inner node, the inputs are again linearly combined and used as output for next node. The inputs and outputs of all the inner nodes are constructed in turn as the same, and the output of root node is the output of mathematical model, implementing segment-linear approximation. With the means of machine learning of pa

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Kaczorowska, D., J. Rezmer, T. Sikorski, and P. Janik. "Application of PSO algorithms for VPP operation optimization." Renewable Energy and Power Quality Journal 17 (July 2019): 91–96. http://dx.doi.org/10.24084/repqjq17.230.

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Kaczorowska, D., J. Rezmer, T. Sikorski, and P. Janik. "Application of PSO algorithms for VPP operation optimization." Energies and Quality Journal 1 (June 2019): 48–53. http://dx.doi.org/10.24084/eqj19.230.

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The random character of generation of distributed energy resources and their increasing numbers cause problems with power balancing in distribution grids. One approach to mitigate the balancing issue is deployment of a virtual power plant VPP with accordingly dimensioned components, i.e. renewable generation and storages. It is also necessary to control the power flow in a way that production and consumption balance would not be threaten. From an investor's perspective, profits and costs are important. The components of a VPP installation, such as generators and storage systems must meet the c

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Novoa-Hernández, Pavel, Carlos Cruz Corona, and David A. Pelta. "Efficient multi-swarm PSO algorithms for dynamic environments." Memetic Computing 3, no. 3 (2011): 163–74. http://dx.doi.org/10.1007/s12293-011-0066-7.

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Frackiewicz, Mariusz, Henryk Palus, and Daniel Prandzioch. "Superpixel-Based PSO Algorithms for Color Image Quantization." Sensors 23, no. 3 (2023): 1108. http://dx.doi.org/10.3390/s23031108.

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Nature-inspired artificial intelligence algorithms have been applied to color image quantization (CIQ) for some time. Among these algorithms, the particle swarm optimization algorithm (PSO-CIQ) and its numerous modifications are important in CIQ. In this article, the usefulness of such a modification, labeled IDE-PSO-CIQ and additionally using the idea of individual difference evolution based on the emotional states of particles, is tested. The superiority of this algorithm over the PSO-CIQ algorithm was demonstrated using a set of quality indices based on pixels, patches, and superpixels. Fur

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