RDEx-CMOP: Feasibility-Aware Indicator-Guided Differential Evolution for Fixed-Budget Constrained Multiobjective Optimization

TL;DR

RDEx-CMOP is a differential evolution algorithm designed for constrained multiobjective optimization, demonstrating superior performance in the CEC 2025 benchmark with fast feasibility, stability, and low violations.

Contribution

It introduces an epsilon feasibility schedule, indicator-driven fitness, and a new mutation operator, advancing constrained multiobjective optimization methods.

Findings

Achieves highest total score in CEC 2025 benchmark.

Exhibits strong target-attainment and near-zero violations.

Outperforms comparison algorithms in stability and feasibility.

Abstract

Constrained multiobjective optimisation requires fast feasibility attainment together with stable convergence and diversity preservation under strict evaluation budgets. This report documents RDEx-CMOP, the differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session) constrained multiobjective track. RDEx-CMOP integrates an {\epsilon}-level feasibility schedule, a SPEA2-style indicator-driven fitness assignment, and a fitness-oriented current-to-pbest/1 mutation operator. We evaluate RDEx-CMOP on the official CEC 2025 CMOP benchmark using the median-target U-score framework and the released trace data. Experimental results show that RDEx-CMOP achieves the highest total score and the best overall average rank among all released comparison algorithms, with strong target-attainment behaviour and near-zero final violation on most problems.