Bare bones differential evolution
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Date
Authors
Omran, Mahamed G.H.
Engelbrecht, Andries P.
Salman, Ayed
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution 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. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.
Description
Keywords
Evolutionary computation, Differential evolution, Particle swarm optimization (PSO), Optimization
Sustainable Development Goals
Citation
Omran, MGH, Engelbrecht, AP & Salman, A 2008, 'Bare bones differential evolution', European Journal of Operational Research, 2008. [doi:10.1016/j.ejor.2008.02.035]