Abstract
Pelican-Optimization Algorithm is a revolutionary meta-heuristic algorithm influenced by societal activities and stalking ability of pelicans. The ingenious chaotic variations of POA have been created to address the challenge of feature-selection for occupational stress diagnosis. In this manuscript, six different chaotic maps have been deployed to POA to enhance its performance. Moreover, the chaotic variants' performance has been contrasted with the original POA. It has been observed from the results that CPOA_4(Chaotic Pelican Optimization Algorithm with Iterative map) delivers the best performance in terms of average accuracy. Whereas, in the case of average execution time except for CPOA_1(Chaotic Pelican Optimization Algorithm with Chebyshev map) all other chaotic variants outperformed the original POA. However, the original POA surpasses other chaotic variants when only the number of features are of prime concern. Additionally, the performance of the six chaotic variants and original POA has been evaluated based on a multi-criterion decision (MCD) matrix.
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