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
The Lock-On Mechanism MPPT algorithm as applied to the hybrid photovoltaic cell and thermoelectric generator system
Trevor Hocksun Kwan,Xiaofeng Wu +1 more
TL;DR: In this article, a lock-on mechanism (LOM) based MPPT algorithm was proposed for the hybrid photovoltaic cell and thermoelectric generator (PV/TEG) system.
Journal ArticleDOI
Improved particle swarm optimization algorithm using design of experiment and data mining techniques
TL;DR: An extension algorithm, namely OLCPSO (Optimal Latin hypercube design and Classification and Regression tree techniques for improving basic PSO), is developed by consciously distributing more particles into potential optimal regions and possesses competitive optimization ability and algorithm stability in contrast to the existing initialization PSO methods.
Journal ArticleDOI
Multi-scale quantum harmonic oscillator algorithm for global numerical optimization
TL;DR: This paper compares MQHOA with several well-known metaheuristic algorithms, such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO) and quantum particle Swarm optimization (QPSO), and indicates the competitive and superior performance of the proposed algorithm in both convergence speed and optimal solution accuracy.
Journal ArticleDOI
Fully Complex Valued Wavelet Network for Forecasting the Global Solar Irradiation
TL;DR: Results obtained throughout this paper show that the FCWN is a promising technique for forecasting daily and hourly solar irradiation.
Journal ArticleDOI
Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs
TL;DR: appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems are suggested and a comparison of these algorithms to each other as well as to the heuristic algorithm is presented.
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.