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

2026-03-27

AI Summary

This work addresses the challenge of efficiently obtaining high-quality solution sets for multi-objective optimization under a fixed evaluation budget. The proposed algorithm, RDEx-MOP, innovatively integrates indicator-guided environmental selection, a niche-preserving Pareto archive, and a complementary differential evolution mutation strategy to effectively balance exploration and exploitation within limited function evaluations. Evaluated on the official CEC 2025 MOP benchmark suite, RDEx-MOP achieves the highest overall score and best average ranking, significantly outperforming all competing algorithms—including its baseline counterpart RDEx—thereby demonstrating superior convergence speed and solution set quality.

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.

Problem

• fixed-budget multiobjective optimization
• convergence speed
• IGD
• target region
• evaluation budget

Innovation

• Indicator-based selection
• Niche preservation
• Differential evolution
• Fixed-budget optimization
• Multiobjective optimization