Smart Starts: Accelerating Convergence through Uncommon Region Exploration

• URL: http://arxiv.org/abs/2505.05661v1
• Date: Thu, 08 May 2025 21:36:14 GMT
• Title: Smart Starts: Accelerating Convergence through Uncommon Region Exploration
• Authors: Xinyu Zhang, Mário Antunes, Tyler Estro, Erez Zadok, Klaus Mueller,
• Abstract summary: This study introduces a hybrid strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL)<n>OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions.<n>This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems.
• Score: 22.806659781921432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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