Investigation on evolutionary computation techniques of a nonlinear system

Authors:
Tran Trong Dao
Affiliations:
Faculty of Information Technology and Applied Mathematics, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Office of Science-Technology, Cooperation and Postgraduate Studies
Venue:
Modelling and Simulation in Engineering
Year:
2011

Citing 7
Cited 0

An introduction to differential evolution

New ideas in optimization
Evolutionary Algorithms in Engineering Applications

Evolutionary Algorithms in Engineering Applications
On Step Width Adaptation in Simulated Annealing for Continuous Parameter Optimisation

Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Scatter search

Advances in evolutionary computing
Comparison of an self-organizing migration algorithm with simulated annealing and differential evolution for automated waveform tuning

Advances in Engineering Software
Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors

Engineering Applications of Artificial Intelligence
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The main aim of this work is to show that such a powerful optimizing tool like evolutionary algorithms (EAs) can be in reality used for the simulation and optimization of a nonlinear system. A nonlinear mathematical model is required to describe the dynamic behaviour of batch process; this justifies the use of evolutionary method of the EAs to deal with this process. Four algorithms from the field of artificial intelligent--differential evolution (DE), self-organizing migrating algorithm (SOMA), genetic algorithm (GA), and simulated annealing (SA)--are used in this investigation. The results show that EAs are used successfully in the process optimization.