Abstract—The Internet of Everything (IoE) enables ubiquitous connectivity among heterogeneous devices but poses significant challenges for task offloading and resource allocation due to high dynamics and strict resource constraints. To address these complexities, optimization plays a pivotal role in efficiently orchestrating these resources. This survey comprehensively reviews optimization methodologies for IoE task offloading. Methodologically, this study relies on a comprehensive literature synthesis to distill evolutionary algorithmic trends and evaluate their applicability to specific IoE scenario constraints. The review is structured around six major paradigms: classic, Lyapunov-based, heuristic, game-theoretic, AI-native, and hybrid optimization. For each, representative techniques, assumptions, and performance characteristics are summarized. Comparative analysis highlights the evolution from model-based formulations toward intelligent, adaptive, and collaborative frameworks. Key challenges are identified, including scalability bottlenecks, learning instability in non-stationary environments, and complex multi-objective trade-offs. Finally, emerging research directions are outlined. This work bridges theoretical foundations with intelligent methodologies, offering a unified perspective for advancing cognitive and sustainable IoE ecosystems.
 
Keywords—adaptive scheduling, decision making, internet of everything, resource allocation, scheduling optimization, task offloading


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