Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques.
@article{arxiv.2505.05661,
title = {Smart Starts: Accelerating Convergence through Uncommon Region Exploration},
author = {Xinyu Zhang and Mário Antunes and Tyler Estro and Erez Zadok and Klaus Mueller},
journal= {arXiv preprint arXiv:2505.05661},
year = {2025}
}