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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Evolutionary Algorithm based Controller Design for Grid tie Inverter to improve the Quality of Power System
TL;DR: The core objective of this paper is to increase the overall efficacy of the inverter operation by designing a new controlling strategy by realizing a novel Monkey King Evolution Algorithm (MKEA).
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Iris boundary localization based on Hough transform and the quadratic circle data compensation
Yu Yang,Jinsong Wang,Yadi Xue +2 more
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Hybrid Optimization Methods Application on Sizing and Solving the Economic Dispatch Problems of Hybrid Renewable Power Systems
TL;DR: In this article , an application of the crow and particle swarm as a hybrid method was examined in a certain region of Libya for a PV/wind hybrid renewable power system, and the results showed that the particle swarm was more effective than the traditional particle swarm.
MODIFIED IMPROVED PARTICLE SWARM OPTIMIZATION (MIPSO) SEBAGAI SOLUSI ECONOMIC DISPATCH PADA SISTEM KELISTRIKAN 500 kV JAWA-BALI
TL;DR: In this article, a modified improved particle swarm optimization (MIPSO) approach with CFBPSO was proposed to minimize the cost of fuel to determine the combination of the output power of each generator.
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Research on fault diagnosis of transformer based on laser induced fluorescence technology
TL;DR: In this paper , a laser-induced fluorescence spectroscopy (LIF) technology was combined with differential mutation brainstorm optimization algorithm (DBSO) to optimize the ELM model to identify transformer fault diagnosis types.
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.