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

2026-03-27

AI Summary

This work proposes a development-biased restart differential evolution algorithm for single-objective numerical optimization under fixed-budget and bound-constrained settings. By integrating Success-History Based Adaptive Differential Evolution (SHADE), a hybrid branching mutation strategy, and lightweight local perturbations, the algorithm effectively balances exploration and exploitation within a limited number of function evaluations. Evaluated on 29 benchmark functions from the CEC 2025 suite using the U-score assessment framework, the proposed method demonstrates statistically significant superiority in both convergence speed and solution accuracy, offering a robust and efficient approach for computationally expensive optimization scenarios.

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.

Problem

• bound-constrained optimization
• single-objective optimization
• fixed-budget optimization
• numerical optimization
• evolutionary algorithms

Innovation

• differential evolution
• exploitation bias
• success-history adaptation
• fixed-budget optimization
• bound-constrained optimization