Smart Starts: Accelerating Convergence through Uncommon Region Exploration

TL;DR

This paper presents a hybrid initialization method for evolutionary algorithms that combines opposition-based learning and empty-space search to improve diversity, accelerate convergence, and enhance solution quality on complex optimization problems.

Contribution

It introduces a novel hybrid initialization strategy that effectively explores under-explored regions, outperforming traditional methods in diverse, high-dimensional optimization tasks.

Findings

Enhanced population diversity and convergence speed.

Superior solution quality on benchmark problems.

Effective exploration of under-explored regions.

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.