G
Gexiang Zhang
Researcher at Chengdu University of Information Technology
Publications - 213
Citations - 4704
Gexiang Zhang is an academic researcher from Chengdu University of Information Technology. The author has contributed to research in topics: Membrane computing & Evolutionary algorithm. The author has an hindex of 31, co-authored 182 publications receiving 3546 citations. Previous affiliations of Gexiang Zhang include Chengdu University of Technology & Northern General Hospital.
Papers
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A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections
TL;DR: A many-objective evolutionary algorithm (MaOEA) based on directional diversity (DD) and favorable convergence (FC) and the enhancement of two selection schemes to facilitate both convergence and diversity is proposed.
Journal Article
A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem
TL;DR: Experimental results show that this evolutionary algorithm performs better than quantum-inspired evolutionary algorithms, for certain arrangements of the compartments of the P system structure utilized.
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Forecasting-Aided Imperfect False Data Injection Attacks Against Power System Nonlinear State Estimation
TL;DR: This letter proposes an imperfect false data injection attack model and its corresponding forecasting-aided implementation method against the nonlinear power system state estimation by introducing an attack vector relaxing error.
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Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots
Xueyuan Wang,Xueyuan Wang,Gexiang Zhang,Ferrante Neri,Ferrante Neri,Tao Jiang,Junbo Zhao,Marian Gheorghe,Florentin Ipate,Raluca Lefticaru +9 more
TL;DR: This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots by using an enzymatic numerical membrane system to integrate two proportional- integral-derivative controllers, where neural networks and experts' knowledge are applied to tune parameters.
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Enhancing distributed differential evolution with multicultural migration for global numerical optimization
TL;DR: An enhanced DDE with Multicultural Migration (DDEM) makes use of two migration selection approaches to maintain a high diversity in the subpopulations, and shows a better or equal performance with respect to its competitors in terms of the quality of solutions, convergence, and statistical tests.