.png){kind=icon}
Return
Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijepes.2013.06.015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Economic dispatch
Emission
Valve point loading
Pareto front
Opposition based learning
Teaching learning based optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5
Papers:
1.1W
Citations:
3.1W
Organization
Cited Papers
Management of Localized Seminoma, Stage I-II: SIU/ICUD Consensus Meeting on Germ Cell Tumors (GCT), Shanghai 2009.png)
Urology
UrologyIF0
PREAI
ShareSaveShareSaveShareSave
ShareSave
View more