Nowadays, mobile edge computing (MEC) is an effective computing model, offers effective computation systems to Internet of Things (IoT). Basically, a placement of MEC servers nearest to the mobile users have significantly minimized the access delays as well as cost of utilizing services. Nevertheless, an edge cloud is mobile and its services are constrained to various neighboring users. Hence, acquiring an ideal offloading policy in the limitations of flexibility and constrained resources poses a complex problem. Thus, this research proposes the Sine Cosine-based Egret Swarm Optimization Algorithm (SC-ESOA) is introduced for an effective resource allocation in MEC. MEC systems are highly dynamic, with different user demands and resource constraints. SC-ESOA's adaptive mechanism which ensures an efficient handling of such variations through modifying resource allocations dynamically. This research also proposes the resource optimization approach for identifying a resource distribution in a system with respect to make sure satisfactory service availability at the less cost. The experimental results show that proposed SC-ESOA attains the minimum energy consumption of 1.65 J and minimum delay of 1.03 s, respectively, as compared to the existing methods like ESOA.