An immune co-evolutionary algorithm based approach for optimization control of gas turbine

Authors:
Xiang-feng Zhang;Jun Liu;Yong-sheng Ding
Affiliations:
Shanghai Dianji University, Shanghai, China;Shanghai Dianji University, Shanghai, China;Donghua University, Shanghai, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 7
Cited 0

An analysis of cooperative coevolutionary algorithms

An analysis of cooperative coevolutionary algorithms
Data mining of Bayesian networks using cooperative coevolution

Decision Support Systems
Immune optimization algorithm for constrained nonlinear multiobjective optimization problems

Applied Soft Computing
Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process

Expert Systems with Applications: An International Journal
Immune-based evolutionary algorithm for fabric evaluation

Mathematics and Computers in Simulation
An antibody network inspired evolutionary framework for distributed object computing

Information Sciences: an International Journal
Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gas turbine is a complex non-linearity system and operates in variable conditions. Traditional control methods are usually adopted in the control loop of gas turbine. The methods may cause control error with the theoretically correct value. In this paper, an immune co-evolutionary algorithm (ICEA) is proposed inspired by immune mechanisms and co-evolutionary computation. And the control of gas turbine is optimized with the ICEA. The procedures of the ICEA mainly include clonal selection and proliferation, fitness evaluation, hyper-mutation, co-evolution and antibody population update. The fitness function is defined referencing to the control model of gas turbine considering some constraints, such as the compressor surge edge constraints and the highest initial gas temperature. Two cases are simulated using the ICEA when the system is accelerated to the partial load and the maximum load, respectively. The simulations show that the ICEA can optimize the quantity of oil to make the gas turbine system reach the terminal status within the shortest time. And the consumed time for the latter is longer than that for the former. The results demonstrate that the ICEA has good feasibility and practicability for the optimization control of gas turbine.