Stasis type particle stability in a stochastic model of particle swarm optimization

2021 ◽  
Author(s):  
Tomasz Kulpa ◽  
Krzysztof Trojanowski ◽  
Krzysztof Wójcik
Keyword(s):  
Particle Swarm Optimization ◽  
Stochastic Model ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Particle Stability ◽  
Type Particle

scholarly journals A review of velocity-type PSO variants

2016 ◽  
Vol 11 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Ivo Sousa-Ferreira ◽  
Duarte Sousa
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Constraint Handling ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Type Particle ◽  
Handling Method

This paper presents a review of the particular variants of particle swarm optimization, based on the velocity-type class. The original particle swarm optimization algorithm was developed as an unconstrained optimization technique, which lacks a model that is able to handle constrained optimization problems. The particle swarm optimization and its inapplicability in constrained optimization problems are solved using the dynamic-objective constraint-handling method. The dynamic-objective constraint-handling method is originally developed for two variants of the basic particle swarm optimization, namely restricted velocity particle swarm optimization and self-adaptive velocity particle swarm optimization. Also on the subject velocity-type class, a review of three other variants is given, specifically: (1) vertical particle swarm optimization; (2) velocity limited particle swarm optimization; and (3) particle swarm optimization with scape velocity. These velocity-type particle swarm optimization variants all have in common a velocity parameter which determines the direction/movements of the particles.

scholarly journals Novel Orthogonal Momentum-Type Particle Swarm Optimization Applied to Solve Large Parameter Optimization Problems

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Jenn-Long Liu ◽  
Chao-Chun Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Cognitive Learning ◽  
Fractional Factorial ◽  
The Novel ◽  
Large Parameter ◽  
Swarm Optimization ◽  
Type Particle

This study proposes an orthogonal momentum-type particle swarm optimization (PSO) that finds good solutions to global optimization problems using a delta momentum rule to update the flying velocity of particles and incorporating a fractional factorial design (FFD) via several factorial experiments to determine the best position of particles. The novel combination of the momentum-type PSO and FFD is termed as the momentum-type PSO with FFD herein. The momentum-type PSO modifies the velocity-updating equation of the original Kennedy and Eberhart PSO, and the FFD incorporates classical orthogonal arrays into a velocity-updating equation for analyzing the best factor associated with cognitive learning and social learning terms. Twelve widely used large parameter optimization problems were used to evaluate the performance of the proposed PSO with the original PSO, momentum-type PSO, and original PSO with FFD. Experimental results reveal that the proposed momentum-type PSO with an FFD algorithm efficiently solves large parameter optimization problems.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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