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The emergence of mobile edge computing (MEC) has brought cloud services to nearby edge servers facilitating penetration of real-time and resource-consuming applications from smart mobile devices at a high rate. The problem of task offloading from mobile devices to the edge servers has been addressed in the state-of-the-art works by introducing collaboration among the MEC servers. However, their contributions are either limited by minimization of service latency or cost reduction. In this paper, we address the problem by developing a multi-objective optimization framework that jointly optimizes the latency, energy consumption, and resource usage cost. The formulated problem is proven to be an NP-hard one. Thus, we develop an evolutionary meta-heuristic solution for the offloading problem, namely WOLVERINE, based on a Binary Multi-objective Grey Wolf Optimization algorithm that achieves a feasible solution …