Hybrid multi-objective evolutionary algorithms for service placement in the computing continuum: a comparative study with genetic traceability

2026-07-14

AI Summary

This study addresses the conflicting objectives of performance, resource utilization, and latency in multi-objective service placement across the edge–fog–cloud continuum by proposing a hybrid island-model-based multi-objective evolutionary algorithm. The approach co-evolves two heterogeneous subpopulations that periodically exchange solutions and introduces an innovative traceable analysis mechanism based on genetic load to quantify each sub-algorithm’s contribution to the final Pareto front. Integrated with NSGA-II, NSGA-III, U-NSGA-III, SMS-EMOA, MOEA/TS, and MOCPO, the proposed method demonstrates statistically significant superiority over individual algorithms across 30 independent runs. Comprehensive evaluation using metrics including Generational Distance (GD), Inverted Generational Distance (IGD), Hypervolume (HV), Spacing (S), and Set Coverage with Traceable Explanation (STE) confirms its advantages in scalability, optimization efficacy, and interpretability.

Abstract

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.

Problem

• service placement
• computing continuum
• multi-objective optimization
• evolutionary algorithms

Innovation

• hybrid MOEA
• computing continuum
• island model
• genetic traceability
• service placement