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Task offloading from many Internet of Things (IoT) devices to a mobile edge computing (MEC) system, consisting of multiple collaborative edge servers (CES), results in reduced task execution delay as well as energy consumption. However, the exploitation of computation resources of CESs incurs additional costs. Existing works in the literature either focused on minimizing execution latency or energy consumption. In this paper, we have developed a task offloading policy that aims at making a trade-off between device utility and execution cost. The utility is defined as a function of task execution latency and energy consumption of energy-harvesting IoT devices. The task offloading problem is formulated as a subset selection problem that makes the desired trade-off. The offloading problem is proven to be NP-hard and thus we develop a meta-heuristic approach, namely SCOPE, inspired by Social Cognitive …