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Journal articles on the topic 'Algoritmus PSO'

Author: Grafiati
Published: 28 June 2021
Last updated: 13 February 2022

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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 (March 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 optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.
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2

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

Lemonge, Afonso Celso de Castro, Patrícia Habib Hallak, and José Pedro Gonçalves Carvalho. "Otimização estrutural de treliças considerando restrições de frequências naturais de vibração." Principia: Caminhos da Iniciação Científica 18, no. 2 (March 5, 2020): 14. http://dx.doi.org/10.34019/2179-3700.2018.v18.29874.

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Este artigo apresenta um Algoritmo Evolucionário (AE) baseado no comportamento de enxame de partículas (Particle Swarm Optimization - PSO) adaptado para a obtenção de soluções de problemas de otimização estrutural com restrições. O PSO é um algoritmo de fácil implementação e competitivo perante os demais algoritmos populacionais inspirados na natureza. Neste artigo, são analisados problemas de otimização estrutural de treliças submetidas a restrições de frequências naturais de vibração. Para o tratamento destas restrições, incorpora-se ao PSO uma técnica de penalização adaptativa (Adaptive Penalty Method - APM), que tem demonstrado robustez e eficiência quando aplicada no tratamento de problemas de otimização com restrições. O algoritmo proposto é validado através de experimentos computacionais em problemas de otimização estrutural amplamente discutidos na literatura.
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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 (November 19, 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 modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency.
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Obando Paredes, Edgar Dario. "Algoritmos genéticos y PSO aplicados a un problema de generación distribuida." Scientia et technica 22, no. 1 (March 30, 2017): 15. http://dx.doi.org/10.22517/23447214.14301.

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En este informe se presentan los resultados de la aplicación de algoritmos genéticos y PSO (Particle Swarm Optimization), para optimizar un problema de generación distribuida (GD) de potencia que debe cumplir ciertas restricciones. Para la implementación del algoritmo genético se utiliza el toolbox de Matlab ya implementado variando algunos parámetros como fracción de mutación, población etc. Lo anterior para compararlo con la función fmincon ya implementada dentro del ambiente Matlab y sacar conclusiones en cuanto a tasa de convergencia y error entre los datos. El Algoritmo PSO fue implementado teniendo en cuenta procesos estocásticos basados en suerte, definiendo propiedades intrínsecas a él, tal como tamaño de población, factor de inercia etc.
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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 (October 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 motoru aplikován na tři produkční evropské motory běžně využívané v ČR: těžký a automobilový přeplňovaný vznětový motor, a atmosférický zážehový motor. Vypočtené průtoky nasávaného vzduchu byly porovnány s různými referenčními metodami. Výsledky ukazují, že nejistota stanovení toku nasáveného vzduchu obecným algoritmem je v řádu 10% pro většinu provozních režimů motoru, kromě případů recirkulace velké části výfukových plynů, kdy nejistota vzrůstá na desítky procent. Desetiprocentní nejistota pro motory bez vysoké míry recirkulace výfukových plynů je přijatelná pro mnohá, zvláště průzkumná a orientační, měření emisí za provozu, a může být snížena kalibrací algoritmu pro daný motor.
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8

Lenin, K. "POLAR PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 6 (June 30, 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 and compared to other algorithms. Simulation results demonstrate good performance of the Polar Particle Swarm optimization (PPSO) algorithm in solving an optimal reactive power problem.
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9

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 (January 31, 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-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.
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10

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 (July 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 will have a different impact on particles’ exploration and exploitation ability. In this paper, the phenomenon of particles gets “stuck in” the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these setting on the algorithm’s ability of exploration and exploitation. From these experimental studies, an algorithm’s ability of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems.
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11

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 perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.
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12

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 (January 31, 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 address these problems. The algorithm’s global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms—a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.
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13

Monteiro, Sildomar T., and Carlos H. C. Ribeiro. "Desempenho de algoritmos de aprendizagem por reforço sob condições de ambiguidade sensorial em robótica móvel." Sba: Controle & Automação Sociedade Brasileira de Automatica 15, no. 3 (September 2004): 320–38. http://dx.doi.org/10.1590/s0103-17592004000300008.

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Analisamos a variação de desempenho de algoritmos de aprendizagem por reforço em situações de ambigüidade de estados comumente produzidas pela baixa capacidade sensorial de robôs móveis. Esta variação é produzida pela violação da condição de Markov, importante para garantir a convergência destes algoritmos. As conseqüências práticas desta violação em sistemas reais não estão avaliadas de maneira definitiva na literatura. São estudados neste artigo os algoritmos Q-learning, Sarsa e Q(lambda), em experimentos realizados em um robô móvel Magellan Pro™. De modo a definir um verificador de desempenho para os algoritmos testados, foi implementado um método para criar mapas cognitivos de resolução variável. Os resultados mostram um desempenho satisfatório dos algoritmos, com uma degradação suave em função da ambigüidade sensorial. O algoritmo Q-learning teve o melhor desempenho, seguido do algoritmo Sarsa. O algoritmo Q(lambda) teve seu desempenho limitado pelos parâmetros experimentais. O método de criação de mapas se mostrou bastante eficiente, permitindo uma análise adequada dos algoritmos.
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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 high stability and a high computing efficiency, but it can’t obtain the problem’s accurate global optimal solutions. For GA, population size and mutation rate are two main parameters affect its solution qualities. Comparative to GA, PSO can obtain the accurate global optimal solutions of the problem with a higher computing efficiency, but with a less stability. For PSO, population size and velocity parameter are two main parameters affect its solution qualities.
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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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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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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 is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.
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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 due attention. Considering the importance of these parameters, this paper presents a preliminary analysis of PSO acceleration coefficients using the conventional and a modified variant of PSO when applied to wind farm layout design. Empirical results show that the acceleration coefficients do have an impact on the quality of final layout, resulting in better overall energy output.
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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 algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.
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Tekchandani, Prakash, and Aditya Trivedi. "Clock Drift Management Using Nature Inspired Algorithms." Journal of Information Technology Research 5, no. 4 (October 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 (DE). The paper estimates the clock offset, clock skew, generates linear line and optimizes the value of average time synchronization error using PSO, Variant of PSO and DE. In this paper we present implementation and experimental results that produces reduced average time synchronization error using PSO, Variant of PSO and DE, compared to that of linear regression used in FTSP.
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Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (March 2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
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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 (February 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 higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.
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Pérez-Archila, Luis Miguel, Juan David Bastidas Rodriguez, and Rodrigo Correa1. "Solución del modelo de un generador fotovoltaico utilizando los algoritmos de optimización Trust Region Dogleg y PSO." Revista UIS Ingenierías 19, no. 1 (January 1, 2020): 37–48. http://dx.doi.org/10.18273/revuin.v19n1-2020003.

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El modelo matemático de un generador fotovoltaico en conexión Serie-Paralelo representado mediante el modelo de diodo simple, tiene asociado a él un sistema de ecuaciones no lineales. En este trabajo se propone la solución de estos sistemas empleando los métodos de optimización Trust Region Dogleg y Optimización por Enjambre de Partículas, para resolver el modelo de un generador fotovoltaico operando en condiciones homogéneas y no homogéneas, variando el número de submódulos y el patrón de sombreado que incide sobre el generador. Se realizó la simulación de los modelos para generadores compuestos por 3 y 15 submódulos en serie, bajo diferentes condiciones de sombreado. De los métodos implementados, Trust Region Doglegmostró un mejor desempeño con tiempos de cómputo 2 y 14 veces menores que el método de referencia y Optimización por Enjambre de Partículas, respectivamente. Y un error medio cuadrático igual o un 50% inferior a los otros métodos
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Pham, Manh-Hai, T.-A.-Tho Vu, Duc-Quang Nguyen, Viet-Hung Dang, Ngoc-Trung Nguyen, Thu-Huyen Dang, and The Vinh Nguyen. "Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam." Energies 12, no. 12 (June 14, 2019): 2283. http://dx.doi.org/10.3390/en12122283.

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Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.
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Nie, Shu Zhi, Yan Hua Zhong, and Ming Hu. "Short-Time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture." Advanced Materials Research 756-759 (September 2013): 2785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2785.

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Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and error analysis of experimental results showed that, the designed algorithms can accurately forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better, can be effectively applied to actual traffic engineering.
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Wu, Jui-Yu. "Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches." Mathematical Problems in Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/256180.

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Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.
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Ali, Zain, Bharat Lal Harijan, Tayab Din Memon, Nazmus Naf, and Ubed-u.-Rahman Memon. "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 9, no. 6 (March 30, 2021): 136–41. http://dx.doi.org/10.35940/ijrte.f5515.039621.

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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 option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.
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Millán-Páramo, Carlos, Adalberto Matoski, and Wellington Mazer. "Algoritmo simulated annealing modificado para diseño óptimo de armaduras con variables continuas." Revista Tecnología en Marcha 30, no. 2 (June 1, 2017): 150. http://dx.doi.org/10.18845/tm.v30i2.3209.

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<p class="p1">En los últimos años, la importancia de los aspectos económicos en el campo de las estructuras ha motivado a muchos investigadores a proponer nuevos métodos para minimizar el peso de las estructuras. En este trabajo, el algoritmo simulated annealing modificado (ASAM), es presentado para resolver la optimización de peso en armaduras con variables continuas. Para evaluar y validar el desempeño de ASAM se abordaron cinco problemas reportados en la literatura especializada. Los resultados de ASAM en comparación con los reportados por otros algoritmos de optimización, muestran que este algoritmo puede generar diseños mejorados y ser utilizado efectivamente en la minimización de peso en armaduras.</p>
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Yan, Xue Song, Qing Hua Wu, Cheng Yu Hu, and Qing Zhong Liang. "Circuit Design Based on Particle Swarm Optimization Algorithms." Key Engineering Materials 474-476 (April 2011): 1093–98. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1093.

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This work investigates the application of Particle Swarm Optimization (PSO) algorithms in the field of evolutionary electronics. PSO was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. PSO achieves its optimum solution by starting from a group of random solution and then searching repeatedly. We propose the new means for designing electronic circuits and introduce the modified PSO algorithm. For the case studies this means has proved to be efficient, experiments show that we have better results.
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Ahmad, Yasir, Mohib Ullah, Rafiullah Khan, Bushra Shafi, Atif Khan, Mahdi Zareei, Abdallah Aldosary, and Ehab Mahmoud Mohamed. "SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks." Complexity 2020 (December 12, 2020): 1–9. http://dx.doi.org/10.1155/2020/6695032.

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Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm’s performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.
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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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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 particle swarm optimization for data from some areas, all the coefficients of mathematical model are achieved for the special. The mathematical model is applied to seismic inversion to interpret stratum by seismic data, approving it very practical.
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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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Novoa-Hernández, Pavel, Carlos Cruz Corona, and David A. Pelta. "Efficient multi-swarm PSO algorithms for dynamic environments." Memetic Computing 3, no. 3 (August 31, 2011): 163–74. http://dx.doi.org/10.1007/s12293-011-0066-7.

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DIOŞAN, LAURA, and MIHAI OLTEAN. "EVOLVING THE UPDATE STRATEGY OF THE PARTICLE SWARM OPTIMISATION ALGORITHMS." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 87–109. http://dx.doi.org/10.1142/s0218213007003217.

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A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions.
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Mohamad, Ayham, Jalal Karimi, and Alireza Naderi. "NEW HEURISTIC ALGORITHMS FOR ROLLING AIR FRAME AERODYNAMIC PARAMETERS ESTIMATION." Aviation 24, no. 1 (April 16, 2020): 20–32. http://dx.doi.org/10.3846/aviation.2020.12092.

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In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.
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Zimolová, Petra. "Peripheral artery disease: what is the best approach? The diagnostic and therapeutic algorithm for clinical practice." Cor et Vasa 52, no. 7-8 (July 1, 2010): 437–40. http://dx.doi.org/10.33678/cor.2010.117.

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Lee, Cheng Kang. "Identifying Significant Control Factors of Particle Swarm Optimization Algorithms in Solving Permutation Flowshop Scheduling Problems." Applied Mechanics and Materials 710 (January 2015): 61–66. http://dx.doi.org/10.4028/www.scientific.net/amm.710.61.

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This paper aims to identify significant control factors of particle swarm optimization (PSO) algorithms in solving permutation flowshop scheduling problems. Control factors of PSO algorithms considered herein include inertial weight, acceleration coefficients, breeding operation, and the amount of particles. The full factorial design method is applied to plan a set of experiments. Each experiment, denoting a specific version of PSO algorithm, is used to solve the test problems, Carlier problems. The searching ability of PSO algorithms is defined by the ratio of the number of times that the optimal makespan is searched to the total number of searching times. To identify significant factors, the analysis of variance (ANOVA) method is used to analyze the results of experiments. According to the results of ANOVA, adopting time-varying acceleration coefficients, breeding operation, and a low amount of particles can advance significantly the searching ability of PSO algorithms. Adopting a high amount of particles can increase significantly the robustness of PSO algorithms. Any two-factor interaction is not significant. Inertia weight is not a significant factor, so any effort to modify inertia weight is unnecessary.
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Rusdi, Muhammad. "KOMPARASI PENGGUNAAN ALGORITMA SUPPORT VECTOR MACHINE DENGAN PARTICLE SWARM OPTIMIZATION DALAM MEMPREDIKSI SUHU UDARA." Technologia: Jurnal Ilmiah 8, no. 4 (October 5, 2017): 277. http://dx.doi.org/10.31602/tji.v8i4.1128.

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Algoritma yang dapat dipakai untuk memprediksi data suhu udara,ada yang sebagian yang sudah diketahui algoritma mana yang memiliki kinerja lebih akurat dan sebagian lagi belum di uji kinerja akurasi dari algoritma tersebut. Untuk hal tersebut algoritma perlu diuji untuk mengetahuinya. Metode yang diusulkan adalah SVM-PSO .metode ini di bandingkan dengan algoritma SVM,SVM-PSO yang sudah di uji akurasinya untuk prediksi data suhu udara. Algoritma yang akan diuji adalahSVM-PSO dan SVM, yang digunakan untuk prediksi suhu udara. Masing-masing algoritma akan implementasikan dengan menggunakan RapidMiner 5.3.Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dengan demikian dapat diketahui algoritma yang lebih akurat. Kata Kunci: Suhu Udara, RMSE, support vector machines,svm-pso prediksi suhu udara.
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Dioşan, Laura, and Mihai Oltean. "What Else Is the Evolution of PSO Telling Us?" Journal of Artificial Evolution and Applications 2008 (January 28, 2008): 1–12. http://dx.doi.org/10.1155/2008/289564.

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Evolutionary algorithms (EAs) can be used in order to design particle swarm optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of designing PSO algorithms, we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the swarm behavior. The rules that have been observed can help us design better PSO algorithms for optimization. We investigate and analyze swarm phenomena by looking into the process of evolving PSO algorithms. Several test problems have been analyzed in the experiments and interesting facts can be inferred from the strategy evolution process (the particle quality could influence the update order, some particles are updated more frequently than others, the initial swarm size is not always optimal).
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Witkowski, Tadeusz. "Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems." Technical Sciences 1, no. 22 (February 7, 2019): 61–74. http://dx.doi.org/10.31648/ts.4348.

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This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real production scheduling problem too. The experiment results indicate that this problem can be effectively solved by PSO and DABC algorithms.
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Baihaqi, Mas Ahmad, Rini Nur Hasanah, and Hadi Suyono. "OPTIMASI PENEMPATAN DISTRIBUTION GENERATION PADA PENYULANG PUJON MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION." Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) 7, no. 1 (April 28, 2020): 38–46. http://dx.doi.org/10.33019/ecotipe.v7i1.1650.

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Pada penelitian ini diusulkan penggunaan algoritma Particle Swarm Optimization (PSO) sebagai metode optimasi dalam peletakan Distributed Generation (DG) pada sistem distribusi 20kV penyulang Pujon yang mempunyai 117 bus dengan panjang saluran 59,65 km, dengan tujuan untuk mengurangi rugi-rugi daya. Selain itu, PSO juga digunakan untuk menentukan besar kapasitas DG yang akan dipasang. Algoritma JAYA digunakan sebagai algoritma pembanding PSO. Algoritma yang diusulkan maupun pembandingnya terbukti berhasil menentukan titik lokasi dan besar kapasitas DG yang akan dipasang.
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43

Liu, Hong Ying. "Utilize Improved Particle Swarm to Predict Traffic Flow." Advanced Materials Research 756-759 (September 2013): 3744–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3744.

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Presented an improved particle swarm optimization algorithm, introduced a crossover operation for the particle location, interfered the particles speed, made inert particles escape the local optimum points, enhanced PSO algorithm's ability to break away from local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and test results showed that, the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better can be effectively applied to actual traffic control.
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44

Pehlivanoglu, Volkan Yasin. "Double surrogate modeling usage in PSO." Aircraft Engineering and Aerospace Technology 89, no. 6 (October 2, 2017): 862–70. http://dx.doi.org/10.1108/aeat-02-2015-0035.

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Purpose The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems. Design/methodology/approach The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison. Findings The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms. Research limitations/implications The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems. Practical implications The proposed algorithm can be applied to different inverse design problems. Originality/value The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.
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Asadi, Morteza, Saeedeh Hamidi Alamdari, and Hamid Khaloozadeh. "Tax Revenues Forecasting By Applying PSO Optimization Algoritm." Journal of Research in Economic Modeling 8, no. 30 (March 1, 2018): 147–69. http://dx.doi.org/10.29252/jemr.8.30.147.

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Liu, Hanmin, Xuesong Yan, and Qinghua Wu. "An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion." Symmetry 11, no. 10 (October 15, 2019): 1291. http://dx.doi.org/10.3390/sym11101291.

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Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search problems, decreasing diversity and leading to the prematurity of the population as the number of evolutionary iterations increases. The pre-stack AVO elastic parameter inversion is nonlinear with slow convergence, while the pigeon-inspired optimisation (PIO) algorithm has the advantage of fast convergence and better optimisation characteristics. In this study, based on the characteristics of the pre-stack AVO elastic parameter inversion problem, an improved PIO algorithm (IPIO) is proposed by introducing the particle swarm optimisation (PSO) algorithm, an inverse factor, and a Gaussian factor into the PIO algorithm. The experimental comparisons indicate that the proposed IPIO algorithm can achieve better inversion results.
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Ghosh, Tarun Kumar, and Sanjoy Das. "Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization." Journal of Information Technology Research 11, no. 4 (October 2018): 72–86. http://dx.doi.org/10.4018/jitr.2018100105.

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Grid computing has been used as a new paradigm for solving large and complex scientific problems using resource sharing mechanism through many distributed administrative domains. One of the most challenging issues in computational Grid is efficient scheduling of jobs, because of distributed heterogeneous nature of resources. In other words, the job scheduling in computational Grid is an NP-hard problem. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. In this article, the authors propose a novel hybrid scheduling algorithm which combines intelligently the exploration ability of Particle Swarm Optimization (PSO) with the exploitation ability of Extremal Optimization (EO) which is a recently developed local-search heuristic method. The hybrid PSO-EO reduces the schedule makespan, processing cost, and job failure rate and improves resource utilization. The proposed hybrid algorithm is compared with the standard PSO, population-based EO (PEO) and standard Genetic Algorithm (GA) methods on all these parameters. The comparison results exhibit that the proposed algorithm outperforms other three algorithms.
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Ramdania, D. R., M. Irfan, F. Alfarisi, and D. Nuraiman. "Comparison of genetic algorithms and Particle Swarm Optimization (PSO) algorithms in course scheduling." Journal of Physics: Conference Series 1402 (December 2019): 022079. http://dx.doi.org/10.1088/1742-6596/1402/2/022079.

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Xu, Jun Hong, Jin Li, Yan Wei Wang, Hong Liang, Dong Chao Tian, Nan Zhang, Zhi Yuan Wang, and Wang Cong. "Image Registration Based on MI and PSO Algorithm." Advanced Materials Research 267 (June 2011): 569–73. http://dx.doi.org/10.4028/www.scientific.net/amr.267.569.

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To improve the performance of image registration technology, a new method based on mutual information and PSO (Particle swarm optimization) is proposed in this paper. The MI (mutual Information) algorithm has been applied on image registration. The PSO algorithm is used to find the maximum MI. Compared the PSO and the POWELL algorithms, the results show that the PSO algorithm performs fairly well compared with the traditional algorithms.
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Ramadhan, Setyoko Prismanu, Hasbi Yasin, and Suparti Suparti. "OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION." Jurnal Gaussian 8, no. 2 (May 30, 2019): 208–19. http://dx.doi.org/10.14710/j.gauss.v8i2.26666.

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Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO
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