Computation Offloading Optimization in Mobile Edge Computing Based on HIBSA

Table 1

The differences between several references and our work.

Related works	Problem formulation	Optimization objectives	Algorithm proposed
[15]	Markov decision process (MDP)	Minimize the overall delay cost of all mobile devices subject to some constraints	The sampling and classification (SAC) based edge site selection (SES) algorithm
[18]	A two-layer optimization framework	Maximize its profit by processing the end-users’ data for each MEC server while maximizing its perceived satisfaction for each end-user	Data offloading and MEC server selection (DO-MECS) algorithm
[19]	Single-objective optimization problem with multiple constraints	Lessen the weighted amount of power consumed by communicating devices subject to some constraints	The Lagrangian suboptimal convergent computation offloading algorithm (LSCCOA)
[20]	Multiobjective optimization problem with multiple constraints based on Pareto	Minimize the total execution time and maximize the total weight under the constraints of communication and computing resources	Bat algorithm
Our method	Multiobjective optimization problem with multiple constraints based on Pareto	Minimize the total execution time and maximize the total weight under the constraints of communication, computing and energy resources	Hybrid immune and bat scheduling algorithm (HIBSA)