| Title |
Optimal Environmental and Economic Operation using Evolutionary Computation and Neural Networks |
| Authors |
이상봉(Rhee, Sang-Bong) ; 김규호(Kim, Kyu-Ho) ; 유석구(You, Seok-Ku) |
| Keywords |
Evolutionary stategy ; Two-Phase neural network ; Hybrid algorithms ; Environmental economic dispatch |
| Abstract |
In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy. |