Particle Swarm Optimization for Pattern Recognition and Image Processing

Omran, Mahamed G. H.; Engelbrecht, Andries P.; Salman, Ayed A.

doi:10.1007/978-3-540-34956-3_6

Cited by 89 publications

(85 citation statements)

References 34 publications

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“…Conceptually speaking, Particle Swarm Optimization (PSO) (Esquivel & Coello Coello, 2003;Kennedy & Eberhart, 1995;Omran, Salman, & Engelbrecht, 2006), which has obvious ties with the EA family, lies somewhere in between GA and EP. Yet unlike GA, PSO has no complicated evolutionary operators such as crossover, selection and mutation and it is highly dependent on stochastic processes.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary artificial neural networks by multi-dimensional particle swarm optimization

et al. 2009

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Section: Introductionmentioning

confidence: 99%

Evolutionary artificial neural networks by multi-dimensional particle swarm optimization

et al. 2009

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“…all algorithms have performed 5000 function evaluations). For K-means, FCM and PSO, the parameters were set as in Omran (2005). For DE, the parameters were set as in Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

Bare bones differential evolution

Omran

Engelbrecht

Salman

2009

European Journal of Operational Research

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181

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a b s t r a c tThe 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.

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“…However, they can be used together to form a method which often leads to the result, even when other methods would be too expensive or difficult to implement. Omran [47], [48] has used particle swarm optimization methods for pattern recognition and image processing. A new clustering method based on PSO is proposed and is applied to unsupervised classification and image segmentation.…”

Section: Swarm Intelligence and Knowledge Discoverymentioning

confidence: 99%

Swarm Intelligence in Data Mining

2006

Studies in Computational Intelligence

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Particle Swarm Optimization for Pattern Recognition and Image Processing

Cited by 89 publications

References 34 publications

Evolutionary artificial neural networks by multi-dimensional particle swarm optimization

Evolutionary artificial neural networks by multi-dimensional particle swarm optimization

Bare bones differential evolution

Swarm Intelligence in Data Mining

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