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

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