F
Ferrante Neri
Researcher at University of Nottingham
Publications - 170
Citations - 7547
Ferrante Neri is an academic researcher from University of Nottingham. The author has contributed to research in topics: Differential evolution & Local search (optimization). The author has an hindex of 41, co-authored 170 publications receiving 6440 citations. Previous affiliations of Ferrante Neri include Polytechnic University of Bari & Instituto Politécnico Nacional.
Papers
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Journal ArticleDOI
Recent advances in differential evolution: a survey and experimental analysis
Ferrante Neri,Ville Tirronen +1 more
TL;DR: Numerical results show that, among the algorithms considered in this study, the most efficient additional components in a DE framework appear to be the population size reduction and the scale factor local search.
Journal ArticleDOI
Memetic algorithms and memetic computing optimization: A literature review
Ferrante Neri,Carlos Cotta +1 more
TL;DR: Several classes of optimization problems, such as discrete, continuous, constrained, multi-objective and characterized by uncertainties, are addressed by indicating the memetic “recipes” proposed in the literature.
BookDOI
Handbook of Memetic Algorithms
TL;DR: This book organizes, in a structured way, all the most important results in the field of MAs since their earliest definition until now, including various algorithmic solutions as well as successful applications.
Journal ArticleDOI
An optimization spiking neural p system for approximately solving combinatorial optimization problems.
TL;DR: An extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking Neural P system, are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems.
Journal ArticleDOI
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
TL;DR: A fast adaptive memetic algorithm (FAMA) is proposed which is used to design the optimal control system for a permanent-magnet synchronous motor and excellent results are obtained in terms of optimality, convergence, and algorithmic efficiency.