  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.
