arrow
Return

Evolutionary programming techniques for economic load dispatch

delete2003-02-01
delete915
PRE
AI
N
Nidul Sinha *
R
R. Chakrabarti
C
Chattopadhyay, RK
DOI:10.1109/TEVC.2002.806788delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part 1, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.
Keywords:
Cauchy mutation
classical evolutionary programming
economic load dispatch
fast evolutionary programming
Gaussian mutation
self-adaptation

Journal

IEEE Transactions on Evolutionary Computation cover
IEEE Transactions on Evolutionary Computation
IF:
12
Papers:
1.8K
Citations:
2.4W

Organization

No organization information available
Cited Papers

Cited Papers

errShare
errSave
err
IF0
err
err0
PREAI
err
errShare
errSave
Leisure time physical activity and dementia risk: a dose-response meta-analysis of prospective studies
err2017-10-22
err0
errOAAI
errWei Xu; Hui Fu Wang; Yu Wan; Chen-Chen Tan; Jin-Tai Yu; Lan Tan
errShare
errSave
errShare
errSave
Effect of the electrical double layer on voltammetry at microelectrodes
err2002-05-01
err0
PREAI
errJohn D. Norton; Henry S. White; Stephen W. Feldberg
errShare
errSave
researcher View more