A Novel Self-Adaptive Mixed-Variable Multiobjective Ant Colony Optimization Algorithm in Mobile Edge Computing

Abstract

Mobile edge computing (MEC) provides physical resources closer to end users, becoming a good complement to cloud computing. The booming MEC brings many multiobjective optimization problems. The paper proposes a multiobjective optimization (MOO) algorithm called SAMOACOMV, which provides a new choice for solving MOO problems of MEC. We improve the ACOMV algorithm that is only suitable for solving mixed-variable single-objective optimization (SOO) problems and propose a MOACOMV algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACOMV algorithm performance on parameter setting, we proposed the SAMOACOMV algorithm using a self-adaptive parameter setting scheme. Furthermore, the paper also designs some mixed-variable MOO benchmark problems for the purpose to test and compare the performance of the SAMOACOMV algorithm. The experiments indicate that the SAMOACOMV algorithm has excellent comprehensive performance and is an ideal choice for solving mixed-variable MOO problems.