TY - GEN
T1 - Economic load dispatch using a chemotactic differential evolution algorithm
AU - Biswas, Arijit
AU - Dasgupta, Sambarta
AU - Panigrahi, Bijaya K.
AU - Pandi, V. Ravikumar
AU - Das, Swagatam
AU - Abraham, Ajith
AU - Badr, Youakim
N1 - Funding Information:
2The Caltech Submillimeter Observatory is operated by the California Institute of Technology under funding from the US National Science Foundation (AST93-13929).
Funding Information:
The authors thank the referee for suggesting many improvements in the presentation of the paper. They are indebted to Geoff Blake for the CSO observations and to the JCMT staff for their support during several observation runs. L. G. M. acknowledges the hospitality of the Sterrewacht Leiden and a ‘‘bezoekersbeurs’’ from the Netherlands Organization for Scientific Research (NWO). Research in astrochemistry in Leiden is supported through a PIONIER grant from NWO.
PY - 2009
Y1 - 2009
N2 - This paper presents a novel stochastic optimization approach to solve constrained economic load dispatch (ELD) problem using Hybrid Bacterial Foraging-Differential Evolution optimization algorithm. In this hybrid approach computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossover of the optimization agents. The proposed methodology easily takes care of solving non-convex economic load dispatch problems along with different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.
AB - This paper presents a novel stochastic optimization approach to solve constrained economic load dispatch (ELD) problem using Hybrid Bacterial Foraging-Differential Evolution optimization algorithm. In this hybrid approach computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossover of the optimization agents. The proposed methodology easily takes care of solving non-convex economic load dispatch problems along with different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.
UR - http://www.scopus.com/inward/record.url?scp=71049134006&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71049134006&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02319-4_30
DO - 10.1007/978-3-642-02319-4_30
M3 - Conference contribution
AN - SCOPUS:71049134006
SN - 3642023185
SN - 9783642023187
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 260
BT - Hybrid Artificial Intelligence Systems - 4th International Conference, HAIS 2009, Proceedings
T2 - 4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009
Y2 - 10 June 2009 through 12 June 2009
ER -