DocumentCode
2754979
Title
Differential Evolution Based Particle Swarm Optimization
Author
Omran, Mahamed G H ; Engelbrecht, Andries P. ; Salman, Ayed
Author_Institution
Dept. of Comput. Sci., Gulf Univ. of Sci. & Technol.
fYear
2007
fDate
1-5 April 2007
Firstpage
112
Lastpage
119
Abstract
A new, almost parameter-free optimization algorithm is developed in this paper as a hybrid of the barebones particle swarm optimizer (PSO) and differential evolution (DE). The DE is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. Results of this algorithm are compared to that of the barebones PSO, Von Neumann PSO, a DE PSO, and DE/rand/1/bin. These results show that the new algorithm provides excellent results with the added advantage that no parameter tuning is needed
Keywords
particle swarm optimisation; barebones particle swarm optimizer; differential evolution; parameter-free optimization; Acceleration; Africa; Birds; Computer science; Convergence; Optimization methods; Particle swarm optimization; Search methods; Stochastic processes; Topology;
fLanguage
English
Publisher
ieee
Conference_Titel
Swarm Intelligence Symposium, 2007. SIS 2007. IEEE
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0708-7
Type
conf
DOI
10.1109/SIS.2007.368034
Filename
4223163
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