Research on Multi-Objective Optimization in Fog Computing focuses on designing strategies and algorithms that simultaneously optimize multiple conflicting objectives—such as latency, energy consumption, cost, resource utilization, and Quality of Service (QoS)—in distributed and resource-constrained fog environments. This area addresses challenges arising from dynamic workloads, heterogeneous edge devices, mobility, and the need for real-time decision-making. Key research directions include heuristic- and metaheuristic-based multi-objective optimization techniques (e.g., genetic algorithms, particle swarm optimization, ant colony optimization), predictive and workload-aware resource allocation, and task scheduling strategies. Other emerging topics involve energy- and cost-aware optimization, latency-sensitive application management, fog–cloud–edge collaborative frameworks, and multi-objective SLA-compliant service provisioning. Additionally, research on adaptive, fault-tolerant, and machine learning-enhanced multi-objective optimization for dynamic and large-scale fog computing systems represents significant avenues for advancing efficient, intelligent, and sustainable fog infrastructures.