Adaptive Constraint-Aware I-NSGA-III for Many-Objective QoS Optimization in Fog–Cloud Computing

Fog–Cloud computing provides distributed computational resources close to IoT data sources, but efficient placement and resource utilization remain challenging because of heterogeneous resources and conflicting Quality of Service (QoS) requirements. In particular, simultaneously optimizing energy consumption, latency, execution cost, Service-Level Agreement (SLA) violations, and reliability leads to a constrained many-objective optimization problem in which conventional evolutionary approaches may suffer from reduced selection pressure, poor convergence, and inadequate diversity. To address these challenges, this paper proposes an Adaptive Constraint-Aware NSGA-III (I-NSGA-III) framework for many-objective QoS optimization in Fog–Cloud computing. The proposed approach extends the NSGA-III framework by integrating adaptive ϵ-constraint handling, feasibility-aware dominance, reference-direction biasing, and Angle Penalized Distance (APD)-based diversity preservation. The optimization simultaneously considers five QoS objectives—namely, energy consumption, latency, execution cost, SLA violations, and unreliability represented by (1−Reliability). Candidate solutions are represented as binary placement configurations and evaluated within a DCNSFog-based Fog–Cloud simulation environment implemented using the iFogSim2/CloudSim framework. The performance of the proposed approach is evaluated against NSGA-II and conventional NSGA-III using 30 independent runs, Hypervolume (HV), Inverted Generational Distance (IGD), statistical significance testing, and ablation analysis. The proposed I-NSGA-III achieves a mean HV of 0.8989±0.0283 and a mean IGD of 0.1677±0.0070, compared with 0.5252±0.0729 and 0.4099±0.0415, respectively, for conventional NSGA-III. The improvements are statistically significant, with a Wilcoxon test yielding p=2.8719×10−11 for both HV and IGD comparisons against the evaluated evolutionary baselines. The results demonstrate that adaptive constraint handling and diversity-oriented selection can substantially improve the quality and distribution of Pareto-optimal QoS trade-offs in many-objective Fog–Cloud optimization. The study also identifies limitations related to the current binary placement representation, the coupling between SLA and reliability measures, static simulation conditions, and the absence of large-scale real-world testbed validation, providing directions for future research.