RDEx-MOP: Indicator-Guided Reconstructed Differential Evolution for Fixed-Budget Multiobjective Optimization

TL;DR

RDEx-MOP is a new differential evolution algorithm designed for fixed-budget multiobjective optimization, excelling in reaching target regions quickly and effectively on benchmark problems.

Contribution

It introduces an indicator-guided, reconstructed differential evolution approach with environmental selection and niche maintenance for improved performance.

Findings

RDEx-MOP achieves the highest total score among comparison algorithms.

It attains the best average rank in the CEC 2025 MOP benchmark.

Experimental results confirm its effectiveness in fixed-budget scenarios.

Abstract

Multiobjective optimisation in the CEC 2025 MOP track is evaluated not only by final IGD values but also by how quickly an algorithm reaches the target region under a fixed evaluation budget. This report documents RDEx-MOP, the reconstructed differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session) bound-constrained multiobjective track. RDEx-MOP integrates indicator-based environmental selection, a niche-maintained Pareto-candidate set, and complementary differential evolution operators for exploration and exploitation. We evaluate RDEx-MOP on the official CEC 2025 MOP benchmark using the released checkpoint traces and the median-target U-score framework. Experimental results show that RDEx-MOP achieves the highest total score and the best average rank among all released comparison algorithms, including the earlier RDEx baseline.