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

Dikshant; Dikshit Chauhan; Chen Hao; Anupam Trivedi; Harikumar Kandath; Senthilnath Jayavelu

cs.NEMay 10, 2026

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

Authors: Dikshant, Dikshit Chauhan, Chen Hao, Anupam Trivedi, Harikumar Kandath, Senthilnath Jayavelu

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.

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Jun 26, 2026 · cs.NEJ/K move · Enter open · S save

DE-2LS: Differential Evolution with Lightweight Late Local Search for Constrained Numerical Optimization

Dikshit Chauhan, Anupam Trivedi

Department of Electrical and Computer Engineering, National University of Singapore

Constrained single-objective numerical optimization requires a careful balance among feasibility, objective convergence, and computational efficiency under a fixed function-evaluation budget. This paper proposes DE-2LS, a late-stage, locally search-enhanced variant of differential evolution built on the RDEx framework. The proposed method preserves the original RDEx components, including mutation and crossover operators, success-history adaptation, archive mechanism, population-size reduction, and

ε

-based constraint handling. A lightweight coordinate-pattern local search is added as a guarded polishing component around the current best solution. It is activated only in the late stage of the run, uses a small evaluation budget, and accepts candidates through a feasibility-aware comparison rule. Ablation results show that the finalized DE-2LS configuration achieves the best U-score among all tested variants, confirming that controlled late-stage refinement is more effective than aggressive or premature local search. In the direct comparison with RDEx, DE-2LS achieves a 5.58% gain in U-score. In the four-algorithm comparison, DE-2LS obtains the highest overall U-score of 80968 and the best total rank of 48 among RDEx, CL-SRDE, and UDE-III. These results indicate that DE-2LS improves the exploitation capability of the RDEx-based search framework while preserving its speed advantage under the combined speed-accuracy scoring criterion. The source code of DE-2LS is available at https://github.com/ChauhanDikshit?tab=repositories.

Parallel Continuous Local Search Constrained Optimization Robust Optimization

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Abstract

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