TY - GEN
T1 - Neural network with migration parallel ga for adaptive control of integrated DE-PSO parameters
AU - Pham, Hieu
AU - Tooyama, Sousuke
AU - Hasegawa, Hiroshi
PY - 2015/1/8
Y1 - 2015/1/8
N2 - This study develops an evolutionary strategy called DEPSO-GANN, which uses an artificial neural network (ANN) based on a parallel genetic algorithm (PGA) with migration for the adaptive control of integrated differential evolution (DE) and particle swarm optimization (PSO) to solve large-scale optimization problems, reduce calculation costs, and improve the stability of convergence towards the optimal solution. This approach combines the global search ability of DE and the local search ability of adaptive system with migration parallel GA. The proposed algorithm incorporates concepts from DE, PSO, PGA and neural networks (NN) to facilitate the adaptive control of parameters. DEPSO-GANN is applied to several numerical benchmark tests with multiple dimensions to evaluate its performance, it is also compared with other evolutionary algorithms (EAs) and memetic algorithms (MAs), which is shown to be statistically significantly superior to other EAs and MAs. We confirm satisfactory performance through various benchmark tests.
AB - This study develops an evolutionary strategy called DEPSO-GANN, which uses an artificial neural network (ANN) based on a parallel genetic algorithm (PGA) with migration for the adaptive control of integrated differential evolution (DE) and particle swarm optimization (PSO) to solve large-scale optimization problems, reduce calculation costs, and improve the stability of convergence towards the optimal solution. This approach combines the global search ability of DE and the local search ability of adaptive system with migration parallel GA. The proposed algorithm incorporates concepts from DE, PSO, PGA and neural networks (NN) to facilitate the adaptive control of parameters. DEPSO-GANN is applied to several numerical benchmark tests with multiple dimensions to evaluate its performance, it is also compared with other evolutionary algorithms (EAs) and memetic algorithms (MAs), which is shown to be statistically significantly superior to other EAs and MAs. We confirm satisfactory performance through various benchmark tests.
KW - Adaptive Plan
KW - Differential Evolution
KW - Neural Network
KW - Parallel Genetic Algorithm
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=84929594060&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929594060&partnerID=8YFLogxK
U2 - 10.1109/EUROSIM.2013.13
DO - 10.1109/EUROSIM.2013.13
M3 - Conference contribution
AN - SCOPUS:84929594060
T3 - Proceedings - 8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013
SP - 13
EP - 18
BT - Proceedings - 8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013
A2 - Al-Dabass, David
A2 - Cant, Richard
A2 - Zobel, Richard
A2 - Al-Begain, Khalid
A2 - Cant, Richard
A2 - Orsoni, Alessandra
A2 - Al-Begain, Khalid
A2 - Orsoni, Alessandra
A2 - Al-Dabass, David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013
Y2 - 10 September 2013 through 13 September 2013
ER -