PMSOMA: optical microscope algorithm based on piecewise linear chaotic mapping and sparse adaptive exploration

The optical microscope algorithm (OMA) is a metaheuristic algorithm that draws inspiration from the magnifying functionality of optical microscopes. This study introduces an enhanced variant of OMA, termed PMSOMA, designed to mitigate the original version's limitations, notably its slow converg...

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Bibliographic Details
Published in:Scientific reports 2024-09, Vol.14 (1), p.20849-27, Article 20849
Main Authors: Guo, Linyi, Gu, Wei
Format: Article
Language:English
Subjects:
639/166
639/705
Algorithms
Convergence
Datasets
Engineering
Engineering problem
Evolution & development
Feature selection
Humanities and Social Sciences
Microscopes
multidisciplinary
Optical microscope algorithm
Optimization
Piecewise linear chaotic mapping
Principles
Science
Science (multidisciplinary)
Sparse adaptive exploration mechanism
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Summary:The optical microscope algorithm (OMA) is a metaheuristic algorithm that draws inspiration from the magnifying functionality of optical microscopes. This study introduces an enhanced variant of OMA, termed PMSOMA, designed to mitigate the original version's limitations, notably its slow convergence rates and vulnerability to local optima. PMSOMA integrates a piecewise linear chaotic map to refine population initialization and augment diversity, alongside a sparse adaptive exploration mechanism to bolster search efficacy. The performance of PMSOMA was rigorously tested using a suite of 50 benchmark functions, the CEC2017 test suite, feature selection datasets, and three classical engineering challenges. The empirical findings confirm that PMSOMA surpasses both the original OMA and competing algorithms by delivering superior solutions, accelerating convergence, and demonstrating enhanced robustness in convergence.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-71828-y