Enhanced Chimp Algorithm and Its Application in Optimizing Real-World Data and Engineering Design Problems

This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both D=10 and D=20. At D=10, it ranks first on all functions (average rank 1.00; 12/12 wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At D=20, the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests (α=0.05) against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion.