PSO+: A new particle swarm optimization algorithm for constrained problems

Kohler, Manoela; Vellasco, Marley; Tanscheit, Ricardo

doi:10.1016/j.asoc.2019.105865

Cited by 72 publications

(31 citation statements)

References 27 publications

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“…PSO is a population-based stochastic technique, inspired by social behavior of bird flocking, designed for solving optimization problems [16,25]. In PSO, a swarm of particles is initially generated with random positions and velocities.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Motion-encoded particle swarm optimization for moving target search using UAVs

Phung

2020

Applied Soft Computing

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Motion-encoded particle swarm optimization for moving target search using UAVs

Phung

2020

Applied Soft Computing

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“…For heuristic variants, numerous researchers have proposed some hybridization variants. In [14], two variations are hybridized in high or low level by means of transmits else co-evolutionary approaches as homogeneous else heterogeneous. Here, the hybridization of PSO [15] with the GWO [16] technique exploiting low-level co-evolutionary integrating hybrid is presented.…”

Section: Hybrid Pso-gwo Algorithmmentioning

confidence: 99%

Optimal Sizing and Siting of Distributed Generators by Hybrid Particle Swarm Optimization-Grey Wolf Optimization Algorithm

Vijayan¹

2020

JCMPS

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In operation of the power system, minimization in loss of the power for Distributed System has implication in minimization of cost of operation. In this work, a new Hybrid Particle Swarm Optimization (PSO), and the Grey Wolf Optimization (GWO) technique, is examined in order to determine the optimal sizing, location, and quantity of Distributed Generators units in Distributed System. The aim of optimal positioning of the Distributed Generators issue is to reduce the loss of power in Distributed System because to experience the restraints like current limits, balance in power, Distributed Generators capacity limits, bus voltage limits, and Distributed Generators penetration limit. In order to solve the issue, the hybrid PSO-GWO algorithm is examined to concurrently discover the optimal sizing and siting of Distributed Generators units. Additionally, the optimal amount of Distributed Generators units can be attained through evaluating optimal consequences from various Distributed Generators in the issue. Finally, the developed approach is examined on the IEEE 33-bus, 69-bus, bus, and 118-bus Radial Distributed System (RDS). The consequence evaluations show that the developed approach can attain superior quality solutions than the conventional approaches for the represented scenarios for the test systems. Furthermore, the hybridization of the proposed method consents to accomplish the search procedure at superior speeds. Hence, the proposed technique is a capable technique in order to solve the issue of the optimal position of Distributed Generators units in Distributed System.

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“…With the development of intelligent control and optimization theory, various kinds of NSI methods have emerged based on neural networks (NNs) [21][22][23]. Kohler et al [24] designed a three-layer deep convolutional neural network (D-CNN), and achieved good local optimal effects by identifying multivariable nonlinear systems with the D-CNN.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear System Identification Method Based on Adaptive Neural Network

2021

CIT. J. comput. inf. technol

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Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

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PSO+: A new particle swarm optimization algorithm for constrained problems

Cited by 72 publications

References 27 publications

Motion-encoded particle swarm optimization for moving target search using UAVs

Motion-encoded particle swarm optimization for moving target search using UAVs

Optimal Sizing and Siting of Distributed Generators by Hybrid Particle Swarm Optimization-Grey Wolf Optimization Algorithm

A Nonlinear System Identification Method Based on Adaptive Neural Network

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