Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems

Kamran Rezaei

doi:10.1016/j.asoc.2024.111650

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Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems

2024-07-01

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Kamran Rezaei

Omid Solaymani Fard *

DOI：10.1016/j.asoc.2024.111650

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Abstract

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This paper introduces a novel meta -heuristic optimization algorithm named Clustering Wavelet Oppositionbased Marine Predators Algorithm (CWOMPA) to address some limitations present in the well -established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function -based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition -based learning strategy for maintaining population diversity. Compared with some recent meta -heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real -world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F -Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F -Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real -world applications, including feature selection for medical datasets.

Keywords:

Marine predators algorithm

Fuzzy clustering

Wavelet basis function

Opposition-based learning

Constrained optimization problems