Particle swarm optimization: An overview
R Poli, J Kennedy, T Blackwell - Swarm intelligence, 2007 - Springer
Particle swarm optimization (PSO) has undergone many changes since its introduction in
1995. As researchers have learned about the technique, they have derived new versions …
1995. As researchers have learned about the technique, they have derived new versions …
Composite particle swarm optimizer with historical memory for function optimization
J Li, JQ Zhang, CJ Jiang… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization
technique. It is characterized by the collaborative search in which each particle is attracted …
technique. It is characterized by the collaborative search in which each particle is attracted …
A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem
In this paper we propose PSO–EDA, a hybrid particle swarm optimization (PSO) with
estimation of distribution algorithm (EDA) to solve permutation flowshop scheduling problem …
estimation of distribution algorithm (EDA) to solve permutation flowshop scheduling problem …
[HTML][HTML] Directed particle swarm optimization with Gaussian-process-based function forecasting
J Jakubik, A Binding, S Feuerriegel - European Journal of Operational …, 2021 - Elsevier
Particle swarm optimization (PSO) is an iterative search method that moves a set of
candidate solution around a search-space towards the best known global and local …
candidate solution around a search-space towards the best known global and local …
Feature selection using enhanced particle swarm optimisation for classification models
In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake
feature selection tasks. The aim is to overcome two major shortcomings of the original PSO …
feature selection tasks. The aim is to overcome two major shortcomings of the original PSO …
Perfectionnement des algorithmes d'optimisation par essaim particulaire: applications en segmentation d'images et en électronique
A El Dor - 2012 - theses.hal.science
La résolution satisfaisante d'un problème d'optimisation difficile, qui comporte un grand
nombre de solutions sous-optimales, justifie souvent le recours à une métaheuristique …
nombre de solutions sous-optimales, justifie souvent le recours à une métaheuristique …
Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment
In this study, an integrated multi-objective production-distribution flow-shop scheduling
problem will be taken into consideration with respect to two objective functions. The first …
problem will be taken into consideration with respect to two objective functions. The first …
Perfectionnement d'un algorithme adaptatif d'Optimisation par Essaim Particulaire: application en génie médical et en électronique
Y Cooren - 2008 - theses.hal.science
Les métaheuristiques sont une famille d'algorithmes stochastiques destinés à résoudre des
problèmes d'optimisation difficile. Utilisées dans de nombreux domaines, ces méthodes …
problèmes d'optimisation difficile. Utilisées dans de nombreux domaines, ces méthodes …
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
This paper presents a novel framework of the estimation of particle swarm distribution
algorithms (EPSDAs). The aim is to effectively combine particle swarm optimization (PSO) …
algorithms (EPSDAs). The aim is to effectively combine particle swarm optimization (PSO) …
Applicability of surrogates to improve efficiency of particle swarm optimization for simulation-based problems
Particle swarm optimization (PSO) is a population-based, heuristic technique based on
social behaviour that performs well on a variety of problems including those with non …
social behaviour that performs well on a variety of problems including those with non …