RDEx-CSOP: Feasibility-Aware Reconstructed Differential Evolution with Adaptive epsilon-Constraint Ranking

TL;DR

RDEx-CSOP is a new constrained differential evolution algorithm that effectively balances feasibility and convergence, achieving top performance in the CEC 2025 benchmark.

Contribution

It introduces a novel combination of success-history adaptation, hybrid search, and an epsilon-constraint mechanism with a time-varying threshold.

Findings

RDEx-CSOP achieves the highest total score in CEC 2025 benchmark.

It demonstrates strong speed and constraint-handling performance.

It outperforms comparison algorithms across 28 benchmark functions.

Abstract

Constrained single-objective numerical optimisation requires both feasibility maintenance and strong objective-value convergence under limited evaluation budgets. This report documents RDEx-CSOP, a constrained differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session). RDEx-CSOP combines success-history parameter adaptation with an exploitation-biased hybrid search and an {\epsilon}-constraint handling mechanism with a time-varying threshold. We evaluate RDEx-CSOP on the official CEC 2025 CSOP benchmark using the U-score framework (Speed, Accuracy, and Constraint categories). The results show that RDEx-CSOP achieves the highest total score and the best average rank among all released comparison algorithms, mainly through strong speed and competitive constraint-handling performance across the 28 benchmark functions.