Title
Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution
Abstract
In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. Motivated by the behavior and the proximity characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid approach that combines the Particle Swarm Optimization and the Differential Evolution algorithm. Particle Swarm Optimization has the tendency to distribute the best personal positions of the swarm near to the vicinity of problem's optima. In an attempt to efficiently guide the evolution and enhance the convergence, we evolve the personal experience of the swarm with the Differential Evolution algorithm. Extensive experimental results on twelve high dimensional multimodal benchmark functions indicate that the hybrid variants are very promising and improve the original algorithm.
Year
DOI
Venue
2010
10.1109/CEC.2010.5585967
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
convergence,evolutionary computation,particle swarm optimisation,cognitive experience,convergence,differential evolution,particle swarm optimization,social experience
Particle swarm optimization,Mathematical optimization,Swarm behaviour,Computer science,Meta-optimization,Swarm intelligence,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Machine learning,Swarm robotics,Metaheuristic
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
17
0.86
References 
Authors
15
3
Name
Order
Citations
PageRank
Michael G. Epitropakis1813.67
Plagianakos, V.P.217313.01
Michael N. Vrahatis317913.96