RDEx-CASK: Cauchy Mutation, Archive, and Stagnation Kick for RDEx-CSOP

2026-05-10

AI Summary

This study addresses the common issues of premature stagnation and insufficient diversity in constrained single-objective optimization algorithms. Building upon the RDEx-CSOP framework, the authors propose three key enhancements: (1) employing an independent truncated Cauchy distribution to sample the second scaling factor, thereby improving exploration capability; (2) maintaining a small archive of feasible solutions to preserve population diversity; and (3) introducing a local enhancement strategy based on individual stagnation counters to dynamically accelerate convergence. The proposed method integrates stagnation detection, dynamic response, Cauchy mutation, and feasibility-guided mechanisms without altering the core structure of the original framework. Evaluated on the CEC CSOP benchmark suite (30-dimensional problems over 25 independent runs), the approach achieves solution quality comparable to RDEx, UDE-III, and CL-SRDE, while significantly accelerating convergence on most test instances.

Abstract

We extend RDEx-CSOP with 3 changes that target stagnation & late-stage variance, plus minor parameter tuning. The second scale factor in the standard branch is sampled independently from a truncated Cauchy. A small feasible-only JADE-style archive (|A|_max = 50) is added & sampled with probability |A|/(|A|+|P|). Per-individual stagnation counter triggers, after 180 no-improvement generations, three local overrides on standard branch: pull toward the global best, lift the archive sampling floor to 0.65, & saturate CR to 0.95 when population success rate is below 0.10. The exploitation biased branch & every other RDEx component are left untouched. On CEC CSOP suite (D=30, 25 runs), RDEx-CASK is competitive with RDEx, UDE-III, & CL-SRDE in feasibility-aware quality & improves time-to-target on most problems.

Problem

• stagnation
• late-stage variance
• constrained optimization
• evolutionary algorithm
• diversity maintenance

Innovation

• Cauchy mutation
• archive mechanism
• stagnation detection
• feasibility-aware optimization
• adaptive parameter control