Problem
Research questions and friction points this paper is trying to address.
Constrained Multiobjective Optimization
Fixed-Budget Optimization
Feasibility Attainment
Convergence
Diversity Preservation
RDEx-CMOP: Feasibility-Aware Indicator-Guided Differential Evolution for Fixed-Budget Constrained Multiobjective Optimization
🤖 AI Summary
This work addresses constrained multi-objective optimization under a fixed evaluation budget by proposing the RDEx-CMOP algorithm. Built upon a differential evolution framework, RDEx-CMOP innovatively integrates an ε-level feasibility scheduling mechanism, a SPEA2-style indicator-based fitness assignment strategy, and a fitness-oriented current-to-pbest/1 mutation operator. This design simultaneously ensures rapid acquisition of feasible solutions while maintaining strong convergence and diversity. Evaluated on the CEC 2025 CMOP benchmark suite, RDEx-CMOP significantly outperforms existing state-of-the-art methods, achieving the highest overall score and best average ranking. Notably, it demonstrates superior target attainment capability on most test problems and yields near-zero final constraint violations.
📝 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 ε-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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Feasibility-Aware Scheduling
Indicator-Guided Evolution
Differential Evolution
Fixed-Budget Optimization
Constrained Multiobjective Optimization
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