Multiplier leadership optimization algorithm (MLOA): unconstrained global optimization approach for melanoma classification

作者：Sukanta Ghosh, A. Singh, Shakti Kumar · 发表于：Discover Internet of Things · 年份：2025 · DOI：10.1007/s43926-025-00168-8 · 被引用次数：17 · 研究领域：Computer Science

This paper proposes the multiplier leadership optimization algorithm, which draws inspiration from multiplier leadership principles to search for and optimize solutions to complex problems effectively. Multiplier leaders possess the unique ability to amplify their teams’ collective intelligence and capabilities. They cultivate an environment of open discussion, creative thinking, accountability, and motivating team members to excel. The proposed algorithm is implemented in MATLAB, and its performance is evaluated on the IEEE Congress on Evolutionary Computation 2021 test bench suite, which consists of 80 benchmark functions. The performance of the proposed approach is compared with those of the other 9 other state-of-the-art optimization algorithms. The proposed algorithm successfully solved 47 out of 80 benchmark functions, demonstrating its superior performance. The computational complexity of proposed algorithm is O(M∗N2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(M * N^2)$$\end{document}, making it efficient for large-scale problems. Furthermore, this paper proposes an MLOA-based approach to evolve the near-optimal architecture of convolution neural networks for Melanoma classification. The proposed approach is implemented in Python and tested on the PH2 and ISIC2020 datasets. The PH2 da...