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

2026-04-04

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.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Constrained Multiobjective Optimization
• Fixed-Budget Optimization
• Feasibility Attainment
• Convergence
• Diversity Preservation

Innovation

• Feasibility-Aware Scheduling
• Indicator-Guided Evolution
• Differential Evolution
• Fixed-Budget Optimization
• Constrained Multiobjective Optimization