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
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms
TL;DR: The experimental results showed that, the Se-PSO algorithm achieved better results in terms of faster convergences in all the testing cases compared to the original PSO algorithm, however, the experimental results further showed the Se -PSO as a promising optimization algorithm method in some other different fields.
Journal ArticleDOI
Double-Slope Solar Still Productivity Based on the Number of Rubber Scraper Motions
Ali Omran Al-Sulttani,Amimul Ahsan,Basim A. R. Al-Bakri,Mahir M. Hason,Nik Norsyahariati Nik Daud,Syazwani Idrus,Omer A. Alawi,Elżbieta Macioszek,Zaher Mundher Yaseen +8 more
TL;DR: In this article , the authors used particle swarm optimization (PSO) algorithm to solve the optimization problem, thereby determining the optimal yields of the double-slope solar still hybrid with rubber scrapers.
Journal ArticleDOI
Solution of Nonlinear Reaction-Diffusion Model in Porous Catalysts Arising in Micro-Vessel and Soft Tissue Using a Metaheuristic
TL;DR: The stability, reliability, and exactness of the proposed technique are established through comparison with the outcomes of the standard numerical procedure with the RK4 method and along with the different performance indices, which are Root-Mean-Square Error (RMSE), (TIC), Absolute Error (AE), and Mean Absolute Deviation (MAD).
Journal ArticleDOI
Parameter extraction of single-diode pv-module model using electromagnetism-like algorithm
TL;DR: In this paper, an evolutionary algorithm was proposed to optimize the parameters under various operation conditions and the root mean square error between the computed current based estimated parameters and experimental PV output current is proposed as a fitness function to obtain the optimal solution.
Book ChapterDOI
On the Effects of Parameter Adjustment on the Performance of PSO-Based MPPT of a PV-Energy Generation System
TL;DR: Simulations of an array with three photovoltaic panels, boost-converter driven, were carried out in order to back the analyzes, and a brief guideline on how to implement PSO-MPPT is presented.
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.