An Enhanced Golden Jackal Optimizer for Multi-Criteria DAG Task Scheduling in a Heterogeneous 6G Air–Ground–Space Integrated Network

Next-generation computing paradigms are redefining the 6G ecosystem by enabling distributed intelligence over an integrated Space–Air–Ground Network (SAGIN) continuum. Despite these advances, scheduling complex applications modeled as Directed Acyclic Graphs (DAGs) within this high-fidelity, three-dimensional infrastructure remains an NP-hard challenge, exacerbated by stochastic mobility, intermittent connectivity, and multi-tenancy resource contention. In this study, we present the Enhanced Golden Jackal Optimizer (EGJO). This training-free metaheuristic jointly optimizes four key objectives: execution time (makespan), local energy consumption, probabilistic task reliability, and operational costs under non-linear congestion-aware pricing. To navigate the non-convex fitness landscape and mitigate premature convergence, a stagnation-aware hybrid strategy is introduced that integrates a Frame Shifting operator for global exploration with a load-balancing intensification heuristic. The performance of EGJO is assessed using high-fidelity simulations that incorporate Gauss-Markov mobility models and LEO orbital constraints. Comprehensive evaluations against six metaheuristics (HHO, WOA, GTO, PSO, GA, and standard GJO) and domain-specific state-of-the-art approaches—including Modified HEFT and Deep Reinforcement Learning (DRL)—demonstrate that EGJO attains a superior Pareto-optimal trade-off and a broader solution spread. Experimental results reveal that EGJO achieves nearly a 33x reduction in makespan compared to GA on large-scale 97-task workloads and reduces handover penalties to 3.0 s, significantly outperforming the 8.5–15.0 s range reported by baseline algorithms. Overall, EGJO achieves up to a 60.5% reduction in the global cost function, with average improvements of 57.67% in makespan and 62.83% in operational costs, ensuring robust and adaptive scheduling in dynamic 6G computing environments.