Journal ArticleDOI
Review article: A review of particle swarm optimization and its applications in Solar Photovoltaic system
Anula Khare,Saroj Rangnekar +1 more
- Vol. 13, Iss: 5, pp 2997-3006
TLDR
Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.Abstract:
Particle swarm optimization is a stochastic optimization, evolutionary and simulating algorithm derived from human behaviour and animal behaviour as well. Special property of particle swarm optimization is that it can be operated in continuous real number space directly, does not use gradient of an objective function similar to other algorithms. Particle swarm optimization has few parameters to adjust, is easy to implement and has special characteristic of memory. Paper presents extensive review of literature available on concept, development and modification of Particle swarm optimization. This paper is structured as first concept and development of PSO is discussed then modification with inertia weight and constriction factor is discussed. Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are also discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.read more
Citations
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Journal ArticleDOI
Comprehensive analysis of maximum power point tracking techniques in solar photovoltaic systems under uniform insolation and partial shaded condition
TL;DR: In this article, an encyclopedic review of MPP Tracking (MPPT) technique is presented, which may overcome the distraction of researchers while selecting MPPT technique because all methods have their unique advantages and disadvantages which requisites a thorough and informative comparative analysis.
Journal ArticleDOI
A Review of Geophysical Modeling Based on Particle Swarm Optimization
TL;DR: A review of the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data is presented in this paper, where the main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed.
Journal ArticleDOI
Short-term solar power prediction using multi-kernel-based random vector functional link with water cycle algorithm-based parameter optimization
TL;DR: An optimal kernel function that comprises a linear combination of weighted local kernel and a global kernel to improve the prediction accuracy of the solar power generation is proposed and it is shown that the MK-RVFLN algorithm attains better performance than many other techniques.
Journal ArticleDOI
Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis
TL;DR: The empirical results demonstrated that the proposed GAPSO-FS and GAFOA-FS can select the best SVM model parameters and a more highly relevant feature subset for the SVM classifier than a single algorithm can, thus improving the classification performance when solving a medical diagnosis problem.
Journal ArticleDOI
Particle swarm optimization for achieving the minimum profile error in honing process
TL;DR: In this article, a particle swarm optimization technique was used to improve the finish of the gears' tooth flank, and the results showed that profile error was minimized and the quality was improved based on a set of strategies that were held simultaneously in the input parameters.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
Particle swarm optimization: developments, applications and resources
TL;DR: Developments in the particle swarm algorithm since its origin in 1995 are reviewed and brief discussions of constriction factors, inertia weights, and tracking dynamic systems are included.
Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.