2023
DOI: 10.1109/tcc.2022.3188926
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Directed Search: A New Operator in NSGA-II for Task Scheduling in IoT Based on Cloud-Fog Computing

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Cited by 17 publications
(9 citation statements)
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References 26 publications
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“…However, there can occur starvation of long tasks. Mousavi et al 17 proposed a D‐NSGA‐II algorithm to minimize energy consumption and response time at the same time while considering the deadline. The experimental results demonstrate its efficacy as compared with other traditional meta‐heuristic algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…However, there can occur starvation of long tasks. Mousavi et al 17 proposed a D‐NSGA‐II algorithm to minimize energy consumption and response time at the same time while considering the deadline. The experimental results demonstrate its efficacy as compared with other traditional meta‐heuristic algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…The optimal solution reduces the computation complexity in the resource allocation procedure and improves the overall performance. A modified version of NSGA‐II is presented in Mousavi et al 30 using a dominant non‐sorting genetic algorithm (DNSGA‐II) to minimize the fog computing environment's response time and energy consumption. The modified approach includes a recombination operator, which provides a better balance between exploitation and exploration characteristics.…”
Section: Related Workmentioning
confidence: 99%
“…One strategy of Edge Computing may be to focus on virtualization and industrialised applications at the edge network. Such an evolution to Edge Computing would be enabled by currently emerging technologies such as Software Defined Networking (SDN), Network Functions Virtualization (NFV) and Information-Centric Networking (ICN) [186]. These new technologies provide novel tools that increase flexibility in designing networks [103].…”
Section: Technology-driven Viewmentioning
confidence: 99%