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

This paper addresses the multi-objective service placement problem in computing continuum environments by proposing a collaborative hybrid island-model MOEA. The method systematically applies heterogeneous hybridization across two experimental campaigns, co-evolving state-of-the-art MOEAs like NSGA-II, NSGA-III, and SMS-EMOA with periodic solution exchange. Across 30 independent runs, the hybrid method outperforms most standalone baselines, with statistical tests confirming significant improvements and traceability analysis revealing non-uniform contributions among islands. This matters because it demonstrates that hybrid cooperation yields scalable, interpretable performance gains for distributed edge-fog-cloud architectures, enabling more efficient service placement in the computing continuum.