Title :
A New Solution to Economic Emission Load Dispatch Using Immune Genetic Algorithm
Author :
Liu, Hong-da ; Ma, Zhong-li ; Liu, Sheng ; Lan, Hai
Author_Institution :
Dept. of Autom. Control, Harbin Eng. Univ.
Abstract :
This paper introduces a kind of immune genetic algorithm which regards objective function as antigen, solution as antibody and updates the population using evolutionary strategy. After economic emission load dispatch which belongs to multi-objective constrained optimization problems is discussed, main processes of immune genetic algorithm to solve this matter is given. Through tests a power system model with five coal-burning generating units, feasibility and validity of this algorithm is proved. And by comparing with genetic algorithm and Hopfield neural network, optimization and quick constringency of this algorithm to solve similar problems are proved
Keywords :
Hopfield neural nets; genetic algorithms; power engineering computing; power generation dispatch; power generation economics; Hopfield neural network; antibody; antigen; coal-burning generating units; economic emission load dispatch; electric power system; immune genetic algorithm; multiobjective constrained optimization; power system model; Artificial intelligence; Constraint optimization; Cost function; Diversity reception; Genetic algorithms; Hopfield neural networks; Immune system; Power generation; Power generation economics; Power system economics; Economic emission load dispatch; Electric power system; Immune genetic algorithm;
Conference_Titel :
Cybernetics and Intelligent Systems, 2006 IEEE Conference on
Conference_Location :
Bangkok
Print_ISBN :
1-4244-0023-6
DOI :
10.1109/ICCIS.2006.252299
