M
Michael N. Vrahatis
Researcher at University of Patras
Publications - 332
Citations - 12133
Michael N. Vrahatis is an academic researcher from University of Patras. The author has contributed to research in topics: Artificial neural network & Particle swarm optimization. The author has an hindex of 49, co-authored 325 publications receiving 11339 citations. Previous affiliations of Michael N. Vrahatis include Cornell University & Artificial Intelligence Center.
Papers
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Journal ArticleDOI
Recent approaches to global optimization problems through Particle Swarm Optimization
TL;DR: A Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.
Journal ArticleDOI
On the computation of all global minimizers through particle swarm optimization
TL;DR: The approaches include transformations of the objective function through the recently proposed deflection and stretching techniques, as well as a repulsion source at each detected minimizer, resulting in an efficient algorithm which has the ability to avoid previously detected solutions and, thus, detect all global minimizers of a function.
Proceedings ArticleDOI
Particle swarm optimization method in multiobjective problems
TL;DR: Critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems.
Book
Particle Swarm Optimization and Intelligence: Advances and Applications
TL;DR: Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields.
Book ChapterDOI
UPSO: A Unified Particle Swarm Optimization Scheme
TL;DR: The Unified Particle Swarm Optimization (UPSO) as mentioned in this paper is a new scheme that harnesses the local and global variants of the standard PSO algorithm, combining their exploration and exploitation abilities.