Welcome to the InfoSci Platform
Reference Hub3
Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem

Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem

Sk Md Ali Bulbul, Provas Kumar Roy
Copyright: © 2014 |Volume: 5 |Issue: 4 |Pages: 16
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466656666|DOI: 10.4018/ijsir.2014100101
Cite Article Cite Article

MLA

Bulbul, Sk Md Ali, and Provas Kumar Roy. "Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem." IJSIR vol.5, no.4 2014: pp.1-16. http://doi.org/10.4018/ijsir.2014100101

APA

Bulbul, S. M. & Roy, P. K. (2014). Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem. International Journal of Swarm Intelligence Research (IJSIR), 5(4), 1-16. http://doi.org/10.4018/ijsir.2014100101

Chicago

Bulbul, Sk Md Ali, and Provas Kumar Roy. "Adaptive Teaching Learning Based Optimization Applied to Nonlinear Economic Load Dispatch Problem," International Journal of Swarm Intelligence Research (IJSIR) 5, no.4: 1-16. http://doi.org/10.4018/ijsir.2014100101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Economic load dispatch (ELD) is a process of calculating real power dispatch by satisfying a set of constraints such a way as fuel cost can be minimized. Inclusion of the effect of valve-points and prohibited operation zones (POZs) in the cost functions make ELD problem a non-linear and non-convex one. For solving ELD in power system a newly proposed evolutionary technique namely adaptive teaching learning based optimization (ATLBO) is presented in this article. TLBO mimics the influence of a teacher on students in a classroom environment by social interaction. ATLBO is an improved version of TLBO which makes TLBO faster and more robust. An adaptive dynamic parameter control mechanism is adopted by the proposed ATLBO algorithm to determine the suitable parameter settings for teaching and learning phases of TLBO algorithm. The proposed ATLBO algorithm is tested in three different cases like 10-unit, 40-unit, and 80-unit systems. A comparison of numerical results with other well established techniques reveals optimization superiority of the proposed scheme both in quality of solution and computational efficiency.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.