A Multi-Strategy Kangaroo Escape Optimization Technique for Global Optimization, Engineering Design, and Near-Infrared Prediction of Praeruptorin Content

The Kangaroo Escape Optimization Technique (KET) combines escape and safe-area searches, but its fixed stage allocation, restricted guidance range, limited refinement of low-ranked individuals, and insufficient use of population-state information can reduce its performance on complex problems. This study develops a Multi-Strategy Kangaroo Escape Optimization Technique (MSKET) through iteration-dependent stage switching, population-proportion-based candidate guidance, selective greedy DE/rand-to-best/1 refinement, and beta-distribution opposition-based global-best guidance. The contribution lies in assigning established mechanisms to specific KET limitations and coordinating them across different stages and population subsets. MSKET was evaluated through 30 independent runs on the 100-dimensional CEC2017 and CEC2020 suites. It ranked first on 22 of 29 CEC2017 functions and 8 of 10 CEC2020 functions, with the best average rank on both suites. Ablation, diversity, and nonparametric statistical analyses further supported the observed performance gains. MSKET also achieved the best overall repeated-run results on the piston–lever and three-bar truss design problems. For near-infrared prediction, MSKET-BP obtained an R2 of 0.86440, an RMSE of 2.1377, and a MAPE of 5.0083% on the testing set. These results indicate improved search and prediction performance within the examined tasks, although at a higher computational cost than KET.