Modified LSHADE-SPACMA with new mutation strategy and external archive mechanism for numerical optimization and point cloud registration

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 6, 2025

Abstract Numerical optimization and point cloud registration are critical research topics in the field of artificial intelligence. The differential evolution algorithm is an effective approach to address these problems, LSHADE-SPACMA, winning CEC2017, a competitive variant. However, LSHADE-SPACMA’s local exploitation capability can sometimes be insufficient when handling challenges. Therefore, this work, we propose modified version LSHADE-SPACMA (mLSHADE-SPACMA) for numerical registration. Compared original approach, work presents three main innovations. First, present precise elimination generation mechanism enhance algorithm’s ability. Second, introduce mutation strategy based on semi-parametric adaptive rank-based selective pressure, which improves evolutionary direction. Third, elite-based external archiving mechanism, ensures diversity population accelerate convergence progress. Additionally, utilize CEC2014 (Dim = 10, 30, 50, 100) CEC2017 test suites experiments, comparing our against: (1) 10 recent CEC winner algorithms, including LSHADE, EBOwithCMAR, jSO, LSHADE-cnEpSin, HSES, LSHADE-RSP, ELSHADE-SPACMA, EA4eig, L-SRTDE, LSHADE-SPACMA; (2) 4 advanced variants: APSM-jSO, LensOBLDE, ACD-DE, MIDE. results Wilcoxon signed-rank Friedman mean rank demonstrate that mLSHADE-SPACMA not only outperforms but also surpasses other high-performance optimizers, except it inferior L-SRTDE CEC2017. Finally, 25 cases from Fast Global Registration dataset applied simulation analysis potential developed technique solving practical problems. code available at https://github.com/ShengweiFu?tab=repositories https://ww2.mathworks.cn/matlabcentral/fileexchange/my-file-exchange

Language: Английский

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