Dynamic multi-objective evolutionary algorithm based on decomposition for test task scheduling problem

Abstract

Test task scheduling problem in the dynamic environment (DTTSP) is an important issue in automatic test system. In this paper, a dynamic multi-objective evolutionary algorithm based on decomposition (DMOEA/D) is proposed to improve the adaptability of the environment changes in test process. The mathematical model considering the arrival of dynamic tasks is proposed based on the Markov decision process. Three standard test functions and two DTTSP examples are used in experiment for illustrating the performance of the proposed algorithm. The results show that the proposed algorithm has good performance in convergence and diversity. Almost all the performance metrics of convergence and diversity obtain stable statistical results. The result of convergence ratio of an algorithm is not good as other metrics because of the slow convergence rate. The results also show that the solutions obtained by DMOEA/D have better Pareto front than the dynamic multi-objective particle swarm optimization algorithm (DMOPSO).