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
An effective maximum power point tracking technique for partially shaded photovoltaic system under rapidly changing atmospheric conditions
TL;DR: In this paper , a hybrid of adaptive particle swarm optimization (APSO) and Nelder Mead (NM) was proposed to track global maximum power point (GMPP) under partial shading conditions.
Journal ArticleDOI
Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization.
TL;DR: In this article, a general hierarchical dispatching strategy of mine water, with the aim of addressing the problems of low reuse rate of coal mine water and insufficient data analysis, is proposed.
Journal ArticleDOI
Sensor Fault Diagnosis Based on Ensemble Empirical Mode Decomposition and Optimized Least Squares Support Vector Machine
TL;DR: A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward.
Journal ArticleDOI
Evolutionary algorithm based control strategy for enhanced operation of multifunction grid connected converters
Journal ArticleDOI
Multi-objective availability and cost optimization by PSO and COA for series-parallel systems with subsystems failure dependencies
TL;DR: In this paper , the multi-objective optimization problem of availability and cost is addressed for series-parallel systems with subsystems failure dependencies in case of strong dependency by applying the particle swarm optimization (PSO) algorithm and the cuckoo optimization algorithm (COA).
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.