2019
DOI: 10.1007/s12652-019-01624-4
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Multi-feature fusion and selection method for an improved particle swarm optimization

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Cited by 18 publications
(9 citation statements)
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“…The velocity is bound to the range of −18 to 18. Most researchers obtained optimal results with a value of '2' for c1 and c2 [43,44]. Therefore, in our methodology, this value is selected for the PSO algorithm.…”
Section: Optimizing the Cnn Architecture Using The Pso Algorithmmentioning
confidence: 99%
“…The velocity is bound to the range of −18 to 18. Most researchers obtained optimal results with a value of '2' for c1 and c2 [43,44]. Therefore, in our methodology, this value is selected for the PSO algorithm.…”
Section: Optimizing the Cnn Architecture Using The Pso Algorithmmentioning
confidence: 99%
“…With the recent advancements of computing devices, huge amount of data has become available in the domain of image processing, pattern recognition, financial analysis, business management, and medical studies [1], [2] amongst others. As a consequence, data dimensionality has increased a lot, which has huge impact on the performance of machine learning and data mining algorithms, both in terms of time and storage requirements.…”
Section: Introductionmentioning
confidence: 99%
“…In contrast, with a low number of control parameters, there are more chances of becoming trapped in local optima. A number of metaheuristic algorithms have been applied to multilevel thresholding [17,18], such as the modified artificial bee colony (MABC) [19] algorithm, the cuckoo search algorithm (CS) [20], improved particle swarm optimization (IPSO) [21], the fuzzy adaptive gravitational search algorithm (FAGSA) [22], hybrid Harris Hawks optimization (HHHO) [23], the improved electromagnetism optimization algorithm (IEMO) [24], wind-driven optimization (WDO) [25], the crow search algorithm (CSA) [26], the improved flower pollination algorithm (IFPA) [27], the improved harmony search algorithm (IHSA) [28], and improved emperor penguin optimization (IEPO) [29]. The main feature of these algorithms is their derivative-free behavior to obtain optimal solutions, which enhances the quality of previous solutions on the basis of exploitative and exploratory inclinations.…”
Section: Introductionmentioning
confidence: 99%