Smart Starts: Accelerating Convergence through Uncommon Region Exploration

2025-05-08

AI Summary

To address the slow convergence and premature convergence to local optima in evolutionary algorithms (EAs) on high-dimensional complex optimization problems—largely attributable to suboptimal population initialization—this paper proposes a hybrid initialization strategy integrating opposition-based learning (OBL) and empty-space-aware search (ESA). For the first time, ESA and OBL are synergistically incorporated into the EA initialization phase to actively explore underexplored “unconventional regions,” thereby significantly enhancing both diversity and structural rationality of the initial population. Experimental evaluations on multiple high-dimensional benchmark functions demonstrate that the proposed strategy achieves, on average, a 37% acceleration in convergence speed and a 21% improvement in solution quality over state-of-the-art initialization methods. The core contribution lies in overcoming the diversity bottleneck inherent in conventional random or uniform initialization, establishing an interpretable, reusable, and structurally principled design paradigm for EA initialization.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Improving evolutionary algorithm convergence speed
• Enhancing population diversity in optimization
• Solving high-dimensional complex optimization problems

Innovation

• Hybrid strategy combining ESA and OBL
• OBL generates diverse initial population
• ESA identifies under-explored regions effectively