TL;DR
An enhanced optimization algorithm—named the Improved Multi-Objective Artificial Hummingbird Algorithm (I-MOAHA)—is proposed to efficiently solve the service placement problem and significantly improves upon the baseline MOAHA algorithm.
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared with baselines.
The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources, confirming the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
Findings confirm that system stability and service quality are bounded by fog density, QoS-aware routing, and real-time load regulation, rather than by mere resource scaling.
Ensuring reliability and real-time performance of task offloading in fog computing remains a critical challenge. To address this, this article considers a dual-objective optimization problem of reliability and execution time for task offloading in energy-constrained fog computing. We first propose a more realistic fog computing system model that incorporates Rayleigh fading. Second, we introduce a reliability and time balanced Pareto ant colony optimization algorithm (RTPACO) based on the Pareto ant colony optimization (PACO algorithm. This algorithm is specifically designed for task offloading scenarios in fog computing. Lastly, we compared RTPACO with other multiobjective optimization algorithms using several metrics, including convergence and diversity (evenness and spread). To evaluate the performance of the algorithms, we employed the widely-used Hypervolume (HV) metric. The experimental results demonstrate that RTPACO consistently achieves a superior Pareto front, with HV improvements ranging from 17.2% to 50.2% compared to existing algorithms.