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
Simultaneous and direct identification of thermophysical properties for orthotropic materials
TL;DR: In this article, a direct and simultaneous estimation method of the main three dimensional thermal diffusivity tensor ( a x, a y, a z ) of orthotropic opaque materials is presented.
Journal Article
Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator
Neha Kapoor,Jyoti Ohri +1 more
TL;DR: An improved PSO tuned Proportional Integral Derivative and Sliding Mode classical controllers have been applied for the motion control problem of the robotic manipulator.
Journal ArticleDOI
Modified particle swarm optimization with novel population initialization
TL;DR: Experimental results indicate that MPSO performs much better than the standard PSO both in terms of the speed of solutions and robustness.
Journal ArticleDOI
Symbiotic organism search algorithm for simulation of J-V characteristics and optimizing internal parameters of DSSC developed using electrospun TiO2 nanofibers
TL;DR: In this article, a simple, robust, and powerful metaheuristic symbiotic organism search (SOS) algorithm was used for simulation of J-V characteristics and optimizing the internal parameters of the dye-sensitized solar cells (DSSCs) fabricated using electrospun 1-D mesoporous TiO2 nanofibers as photoanode.
Journal ArticleDOI
Damage Identification of Bridge Based on Modal Flexibility and Neural Network Improved by Particle Swarm Optimization
TL;DR: The optimization of bias and weight for neural network by fitness function of PSO algorithm can realize favorable damage severity identification and possesses more satisfactory accuracy than traditional BP network.
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.