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
More filters
Journal ArticleDOI
Nature inspired algorithms for the solution of inverse heat transfer problems applied to distinct unsteady heat flux orientations in cylindrical castings
Journal ArticleDOI
Application of an Optimized PSO-BP Neural Network to the Assessment and Prediction of Underground Coal Mine Safety Risk Factors
Jiankang Liu,Jian Hao +1 more
TL;DR: Wang et al. as discussed by the authors developed a model for evaluating safety risks in underground coal mines based on the optimized particle swarm optimization-backpropagation (PSO-BP) neural network.
Journal ArticleDOI
Swarming Computational Approach for the Heartbeat Van Der Pol Nonlinear System
Muhammad Umar,Fazli Amin,Soheil Salahshour,Thongchai Botmart,Wajaree Weera,Prem Junswang,Zulqurnain Sabir +6 more
TL;DR: In this paper , a stochastic framework for the numerical treatment of the Van der Pol heartbeat model (VP-HBM) using the feedforward artificial neural networks (ANNs) under the optimization of particle swarm optimization (PSO) hybridized with the active set algorithm (ASA), i.e., ANNs-PSO-ASA.
Journal ArticleDOI
An Innovative Technique for Energy Assessment of a Highly Efficient Photovoltaic Module
Filippo Spertino,Gabriele Malgaroli,Angela Amato,Muhammad Aoun Ejaz Qureshi,Alessandro Ciocia,Hafsa Siddiqi +5 more
TL;DR: In this article , the authors proposed an innovative technique to assess the generated energy by PV modules starting from the knowledge of their equivalent parameters, which can be applied to a highly efficient PV generator with all-back contact, monocrystalline silicon technology, and rated power of 370 W.
Journal ArticleDOI
An Investigation of Logarithm Decreasing Inertia Weight Particle Swarm Optimization in Global Optimization Problem
TL;DR: This research investigates Logarithm Decreasing Inertia Weight (LogDIW) to improve the performance of Particle Swarm Optimization (PSO) and shows that LogDIW achieves better performance than the other PSO variants.
References
More filters
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.