## 5 Joint Optimization of Resource Allocation and Computation-Offloading Decisions
### The Proposed Algorithm JROPSO
Our simulations confirmed the efficiency of the algorithm and its superior performance relative to other methods. In addition, this paper showed that the PSO algorithm is used to solve the optimization problem of computation offloading with highly competitive performance. In future work, the comparison of our proposed algorithm with recent meta-heuristic algorithms like Ant Colony Optimization (ACO), While Optimization Algorithm (WOA), and Harris Hawks Optimization (HHO) is also promising. Moreover, we will involve investigation of PSO applications in non-orthogonal multiple-access-assisted MEC systems. [PERSON] supervised the study; [PERSON] proposed the conceptualization; [PERSON] developed the methodology; [PERSON] and [PERSON] analyzed the algorithm; [PERSON] programmed the software; [PERSON] wrote the original draft of the manuscript; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] participated in manuscript review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00294, Service mobility support distributed cloud technology). The authors declare no conflict of interest. Figure 6: The effect of data size on task offloading. ## References

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