AI 中文总结
研究计算连续体中多目标服务放置问题，采用协作混合岛模型MOEA，通过两个实验活动进行异构混合的系统应用与分析，结合多种指标评估，结果表明混合方法优于多数独立基线，各岛贡献不均，有效验证了混合合作。
AI 中文摘要
本文通过协作混合岛模型多目标进化算法（MOEA）解决计算连续体环境中的多目标服务放置问题。关键创新并非设计新的通用混合算法，而是通过两个独立实验活动，对该特定优化领域进行异构混合的系统应用与分析。第一个活动使用四种先进的MOEA（NSGA-II、NSGA-III、U-NSGA-III和SMS-EMOA），第二个活动使用基于NSGA-II、MOEA/TS和MOCPO的互补混合配置，两者共同进化并定期交换解决方案。这些设计实现跨岛的互补搜索行为，与计算连续体的分布式边缘-雾-云架构自然契合，便于可扩展的并行执行。为评估该方法，定义两个研究假设：一是混合合作是否比独立算法有显著性能提升；二是所有组成算法对最终结果的贡献是否相同。结合标准帕累托前沿质量指标与基于遗传负载的可追溯性分析，量化每个岛对进化解决方案的贡献。在30次独立运行中，混合方法优于大多数独立基线，统计测试证实有显著改进。结果还显示各岛贡献不均，为有效的混合合作提供可解释证据。
英文摘要
This paper addresses multi-objective service placement in computing continuum environments through a collaborative hybrid island-model MOEA. The key innovation is not the design of a new general hybrid algorithm, but the systematic application and analysis of heterogeneous hybridization for this specific optimization domain through two independent experimental campaigns: a first one with four state-of-the-art MOEAs (NSGA-II, NSGA-III, U-NSGA-III, and SMS-EMOA), and a second one with a complementary hybrid configuration based on NSGA-II, MOEA/TS, and MOCPO, both co-evolving and periodically exchanging solutions. These designs enable complementary search behaviors across islands and are naturally aligned with the distributed edge-fog-cloud architecture of the computing continuum, facilitating scalable parallel execution. To evaluate the approach, we define two research hypotheses: (i) whether hybrid cooperation yields significant performance gains over standalone algorithms, and (ii) whether all constituent algorithms contribute equally to the final outcomes. We combine standard Pareto-front quality indicators (GD, IGD, HV, S, and STE) with a traceability-oriented analysis based on genetic load, which quantifies the contribution of each island to the evolved solutions. Across 30 independent runs, the hybrid method outperforms most of the standalone baselines, and statistical tests confirm significant improvements. Results also show non-uniform contributions among islands, providing interpretable evidence of effective hybrid cooperation.
Journal refCluster Computing 29, 456 (2026)
DOI：10.1007/s10586-026-06273-9
