AbstractThis study aims to identify optimal parameter configurations in Differential Evolution (DE) by considering population diversity and dimension size. The goal is to select control parameters that balance exploitation rate and algorithm performance. The proposed approach introduces two adaptation stages - microadaptation and macroadaptation - to influence offspring generation and maintain population diversity. An adaptive population sizing scheme called Self-Adaptive Ensemble-based DE with Enhanced Population Sizing (SAEDE-EP) further enhances diversity. Microadaptation and macroadaptation are triggered based on individual fitness and solution stagnation. SAEDE-EP was evaluated on 26 benchmark functions, the CEC 2019 challenge suite, and 57 constrained optimization benchmarks from CEC 2020. Findings showed SAEDE-EP achieved efficient optimization across unimodal, multimodal, hybrid, and composition problems without exhaustive parameter tuning. Comparative analysis indicated that SAEDE-EP .....