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
More filters
Journal ArticleDOI
Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm
TL;DR: An adaptive random disturbance PSO algorithm that can achieve higher precision in the lithium-ion battery behavior, and it is feasible for wide applications in industry is proposed.
Journal ArticleDOI
Novel continuous function prediction model using an improved Takagi-Sugeno fuzzy rule and its application based on chaotic time series
TL;DR: A novel continuous function prediction model (CFPM) is proposed to resolve prediction problem whose input and output are both continuous functions (CFs), and use an improved Takagi-Sugeno fuzzy rule to predict output CF by optimizing the tendency of input CFs.
Journal ArticleDOI
An efficient two-level swarm intelligence approach for RNA secondary structure prediction with bi-objective minimum free energy scores
TL;DR: Simulation results for TL-PSOfold show that it yields higher prediction accuracy than all the compared approaches and is supported by the non-parametric statistical significance testing using Kruskal–Wallis test followed by post-hoc analysis.
Proceedings ArticleDOI
Classification recognition of anchor rod based on PSO-SVM
TL;DR: The experimental results show that the proposed method is effective in identification of anchor, and the highest prediction accuracy can reach 93.333%.
Journal ArticleDOI
A Novel Honey-Bees Mating Optimization Approach with Higher order Neural Network for Classification
Janmenjoy Nayak,Bighnaraj Naik +1 more
TL;DR: A hybrid metaheuristic honey bee mating based Pi-Sigma Neural Network (PSNN) have been proposed to successfully solve the classification problem of data mining and is compared with other techniques like GA, DE, and PSO.
References
More filters
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.