DocumentCode :
3318041
Title :
Economic load dispatch using bacterial foraging technique with particle swarm optimization biased evolution
Author :
Saber, Ahmed Y. ; Venayagamoorthy, Ganesh K.
Author_Institution :
Dept. of Electr. & Comput. Eng., King Abdulaziz Univ., Jeddah
fYear :
2008
fDate :
21-23 Sept. 2008
Firstpage :
1
Lastpage :
8
Abstract :
This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods.
Keywords :
load dispatching; particle swarm optimisation; cell-to-cell attraction; economic load dispatch; elimination-dispersal steps; high dimensional ELD problems; modified bacterial foraging technique; particle swarm optimization; scalable ELD problems; Gradient methods; Hopfield neural networks; Microorganisms; Neural networks; Optimization methods; Particle swarm optimization; Power generation economics; Power system economics; Space technology; USA Councils; Bacterial foraging technique; economic load dispatch; particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Swarm Intelligence Symposium, 2008. SIS 2008. IEEE
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-2704-8
Electronic_ISBN :
978-1-4244-2705-5
Type :
conf
DOI :
10.1109/SIS.2008.4668291
Filename :
4668291
Link To Document :
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