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arXiv · 2505.05661

Smart Starts: Accelerating Convergence through Uncommon Region Exploration

Xinyu Zhang·Mário Antunes·Tyler Estro·Erez Zadok·Klaus Mueller
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Abstract

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.

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Xinyu Zhang, Mário Antunes, Tyler Estro, Erez Zadok, Klaus Mueller. 2025-05-08. Smart Starts: Accelerating Convergence through Uncommon Region Exploration. https://doi.org/10.1145/3712255.3726720

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