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.
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;
Conference_Titel :
Swarm Intelligence Symposium, 2007. SIS 2007. IEEE
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0708-7
DOI :
10.1109/SIS.2007.368034
