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
Economic performance of membrane distillation configurations in optimal solar thermal desalination systems
Vasiliki Karanikola,Vasiliki Karanikola,Sarah E. Moore,Akshay Deshmukh,Robert G. Arnold,Menachem Elimelech,A. Eduardo Sáez +6 more
TL;DR: In this article, the authors provided a comprehensive evaluation of the economic performance and viability of solar membrane distillation (MD) systems and provided a process model based on mass and energy balances to find the minimum cost of water in MD systems.
Journal ArticleDOI
Optimal foraging algorithm for global optimization
Guang-Yu Zhu,Wei-Bo Zhang +1 more
TL;DR: The results obtained by experiments and Kruskal-Wallis test indicate that the performance of OFA is better than the other six algorithms in terms of the ability to converge to the optimal or the near-optimal solutions, and the second-best one from the view of the statistical analysis.
Journal ArticleDOI
Opposition-based JAYA with population reduction for parameter estimation of photovoltaic solar cells and modules
Xi Yang,Wenyin Gong +1 more
TL;DR: Experimental results tested over several different PV models demonstrate the excellence of EJAYA on accuracy, stability, and convergence speed and suggest it is superior to become an alternative for the parameter detection of PV cells and modules at various practical conditions.
Journal ArticleDOI
A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking
TL;DR: The evaluation through simulations shows that the proposed intelligent hybrid algorithm (proactive detection and adaptive reaction) can quickly and effectively respond and mitigate DoS attacks in adverse conditions in terms of the applied performance criteria.
Journal ArticleDOI
Unsupervised constrained neural network modeling of boundary value corneal model for eye surgery
TL;DR: A numerical computing technique is developed for solving the nonlinear second order corneal shape model (CSM) using feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and active-set algorithms (ASA), which establishes the worth of the scheme in terms of convergence and accuracy.
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.