ARQ: A Cohesive Optimization Design for Stable Performance on Noisy Landscapes

The proposed Adaptive RTR with Quarantine (ARQ) method integrates, within a single evolutionary scheme for continuous optimization, three mature ideas of pbest differential evolution with an archive, success-history parameter adaptation, and restricted tournament replacement (RTR) and extends them with a novel outlier quarantine mechanism. At the heart of ARQ is a combination of the following complementary mechanisms: (1) an event-driven outlier-quarantine loop that triggers on robustly detected tail behavior, (2) a robust center from the best half of the population to which quarantined candidates are gently repaired under feasibility projections, (3) local RTR-based replacement that preserves spatial diversity and avoids premature collapse, (4) archive-guided trial generation that blends current and archived differences while steering toward strong exemplars, and (5) success-history adaptation that self-regulates search from recent successes and reduces manual fine-tuning. Together, these parts sustain focused progress while periodically renewing diversity. Search pressure remains focused yet diversity is steadily replenished through micro-restarts when progress stalls, producing smooth and reliable improvement on noisy or rugged landscapes. In a comprehensive benchmark campaign spanning separable, ill-conditioned, multimodal, hybrid, and composition problems, ARQ was compared against leading state-of-the-art baselines, including top entrants and winners from CEC competitions under identical evaluation budgets and rigorous protocols. Across these settings, ARQ delivered competitive peak results while maintaining favorable average behaviour, thereby narrowing the gap between best and typical outcomes. Overall, this design positions ARQ as a robust choice for practical performance and consistency, providing a dependable tool that can meaningfully strengthen the methodological repertoire of the research community.