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
Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification.
TL;DR: In this paper , a machine learning framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification, and the XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions.
Proceedings ArticleDOI
Voltage stability improvement by demand response
Mitsuki Sagara,Ryuto Shigenobu,Oludamilare Bode Adewuyi,Atsushi Yona,Tomonobu Senjyu,Mir Sayed Shah Danish,Toshihisa Funabashi +6 more
TL;DR: A method to improve the Voltage stability of power system by applying demand response, DR to control the active power of each load bus based on the voltage stability index is proposed.
Book ChapterDOI
Decomposition and Merging Co-operative Particle Swarm Optimization with Random Grouping
Posted ContentDOI
Dynamical modeling and Identification of Li-ion batteries based on Squirrel Search Optimization Algorithm for Electric Vehicle Applications
TL;DR: In this article , an advanced method for modeling and parameter identification of the lithium-ion (Li-ion) battery using an experimental characterization has been presented, where each model parameters are then tested and validated using experimental data obtained from a real test bench.
Dissertation
Development of Maximum Power Extraction Algorithms for PV system With Non-Uniform Solar Irradiances
TL;DR: In this article, a global optimization approach based on grey wolf optimization is proposed to obtain the global maximum power point (MPP) of a PV module under non-uniform solar irradiances.
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.