The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm's effectiveness is verified through simulation experiments.

翻译：移动边缘计算（MEC）系统部署于用户附近，使得移动智能设备能够将计算任务卸载至边缘服务器，从而享受低时延的计算服务。云服务提供商与用户均追求更全面的解决方案，需要在服务部署与任务卸载中审慎权衡多个目标。本研究探讨多用户环境下的服务部署与任务卸载挑战，将其建模为边缘环境中的多任务高维多目标优化（MT-HD-MOO）问题。为确保服务提供的稳定性，除将时延、能耗与成本作为部署目标外，还加入网络可靠性指标。此外，为促进边缘服务器的公平使用，在时延、能耗与成本的基础上，引入负载均衡作为任务卸载的第四个优化目标。本文设计了一种基于多选择策略的MT-HD-MOO算法，用于求解该模型及其解。通过采用多样化选择策略，构建环境选择策略池，以增强高维目标空间中种群的多样性。最终，通过仿真实验验证了算法的有效性。