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

arXiv: https://arxiv.org/abs/2603.27092 · HTML: https://mlsanity.com/paper/2603.27092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP](https://mlsanity.com/paper/2006.02591/) (2020)
- [A Two-stage Evolutionary Framework For Multi-objective Optimization](https://mlsanity.com/paper/2407.06536/) (2024)

## Abstract (arXiv)

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.