RDEx-SOP: Exploitation-Biased Reconstructed Differential Evolution for Fixed-Budget Bound-Constrained Single-Objective Optimization

TL;DR

RDEx-SOP is a new exploitation-biased differential evolution algorithm that balances convergence speed and solution quality, demonstrating competitive performance on the CEC 2025 benchmark.

Contribution

It introduces an exploitation-biased success-history differential evolution variant with hybrid and local perturbation strategies for fixed-budget optimization.

Findings

Achieves strong overall performance on CEC 2025 benchmark

Statistically competitive final outcomes across 29 functions

Effectively balances convergence speed and solution quality

Abstract

Bound-constrained single-objective numerical optimisation remains a key benchmark for assessing the robustness and efficiency of evolutionary algorithms. This report documents RDEx-SOP, an exploitation-biased success-history differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session). RDEx-SOP combines success-history parameter adaptation, an exploitation-biased hybrid branch, and lightweight local perturbations to balance fast convergence and final solution quality under a strict evaluation budget. We evaluate RDEx-SOP on the official CEC 2025 SOP benchmark with the U-score framework (Speed and Accuracy categories). Experimental results show that RDEx-SOP achieves strong overall performance and statistically competitive final outcomes across the 29 benchmark functions.