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
Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms
TL;DR: In this paper, the performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry, and the optimum performance will be achieved by optimized geometry, according to this fact, the multi objective optimization of the HATCT is carried out by using four different multi-objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory.
Journal ArticleDOI
Application of neural network and weighted improved PSO for uncertainty modeling and optimal allocating of renewable energies along with battery energy storage
TL;DR: Generating section includes wind, solar and wave energies in which wind speed and solar irradiance are uncertain parameters and information of Hormoz Island, Iran is the input of problem, and DANN is trained by three adaptive techniques to minimize the prediction error dynamically.
Journal ArticleDOI
Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review
Rasel Ahmed,Shuhaimi Mahadzir,Nor Erniza Mohammad Rozali,Kallol Biswas,Fahad Matovu,Kamran Ahmed +5 more
TL;DR: This comprehensive review presents state-of-the-art theory and application of the most widely used CI techniques such as GA, PSO, SA, DE, HTS, CRO, MOGA, and NSGA II in the optimization of various refrigeration systems.
Journal ArticleDOI
Applying particle swarm optimization algorithm to roundness error evaluation based on minimum zone circle
TL;DR: It is concluded that the Novel PSO–MZC results are a little smaller than the LSC-based results, which verifies that the novel PSO algorithm is feasible to calculate roundness error and the fact that a L SC-based one is generally larger than a MZC-based result is verified.
Journal ArticleDOI
Health parameter monitoring via a novel wireless system
Wen-Tsai Sung,Kuo-Yi Chang +1 more
TL;DR: WSNs technology to transfer physiological data to the cloud for analysis, processing, and storage and the IPSO scheme is used to increase the efficiency and accuracy when searching for at-risk groups, searching data, and defining and summing the weights of physiological data.
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.