English(EN) Local Budget Benefits, Population Commitment, and Solver Performance: A DGO Case Study with Controlled DE Transfer

新的 DGO 优化器分析本地搜索、种群和求解器性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-30 00:39

本文介绍了大狗滚（Da Gou Gun，DGO）优化方法，这是一种种群优化器，旨在分析已评估候选者对局部搜索步骤、种群动态和整体求解器性能的影响。该研究区分了这三种效应，并提出了一个局部改进的承诺可能导致更差结果的案例。在 BBOB 函数上的比较表明，DGO 的 JQ100 配置在总体上优于经过测试的经典策略，尽管 L-SHADE 和 CMA-ES 在特定的初始化和停止规则下表现更好。 AI

排序理由该项目是一篇研究论文，详细介绍了一种新的优化方法及其比较分析。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Shiqi Li · 2026-10-30 00:39

本地预算收益、人口承诺与求解器性能：一项受控 DE 迁移的 DGO 案例研究

An evaluated candidate can improve a local search step, alter the population used by later steps, and affect a solver's final result. We distinguish these three questions using Da Gou Gun Optimization (DGO), a population optimizer designed for this study. Its JQ100 configuration …

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本地预算收益、人口承诺与求解器性能：一项受控 DE 迁移的 DGO 案例研究

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