Advances, Systems and Applications
Table 2 Meta-Heuristics used in fog integrated cloud environment
| Â | Research | Computing model | Optimization technique used | Objective |
|---|---|---|---|---|
Single Objective | [47] | Fog | Harris Hawks technique | Energy-saving |
[48] | Fog-Cloud | Genetic Algorithm | Make-span | |
[49] | Fog-Cloud | Moth-Flame Optimization | Total task execution time | |
[50] | Fog-Cloud | Genetic Algorithm (GA) | Latency | |
Multiobjective | [51] | Fog | Harris Hawks Technique | Energy Consumption, make-span, cost, flow time, and emission rate of CO2 |
[52] | Fog | Genetic Algorithm | make-span, flow time, fitness function, carbon dioxide emission rate, and energy consumption | |
[53] | Fog | Ant colony algorithm | Make-span, response time, and energy usage | |
[54] | Fog-Cloud | Particle Swarm Optimization and Cat Swarm Optimization | Execution time, energy consumption, and average response time | |
[55] | Fog-Cloud | Genetic Algorithm | Computation time, energy consumption, and more percentage of jobs that meet deadlines | |
[56] | Fog-Cloud | Discrete Non-Dominated Sorting Genetic Algorithm II | Makespan and costs | |
[57] | Fog-Cloud | Cultural Evolution Algorithm (CEA) and the Invasive Weed Optimization (IWO) | Makespan and energy consumption | |
[58] | Fog | Whale Optimization Algorithm | Energy consumption and cost |