TY - GEN
T1 - Dynamic multi-objective evolutionary algorithm based on decomposition for test task scheduling problem
AU - Lu, Hui
AU - Xu, Xin
AU - Zhang, Mengmeng
AU - Yin, Lijuan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/20
Y1 - 2016/1/20
N2 - 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).
AB - 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).
KW - Markov decision process
KW - decomposition
KW - dynamic optimization
KW - multi-objective optimization
KW - test task scheduling problem
UR - https://www.scopus.com/pages/publications/84963877183
U2 - 10.1109/ICICIP.2015.7388136
DO - 10.1109/ICICIP.2015.7388136
M3 - 会议稿件
AN - SCOPUS:84963877183
T3 - Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015
SP - 11
EP - 18
BT - Proceedings of 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Intelligent Control and Information Processing, ICICIP 2015
Y2 - 26 November 2015 through 28 November 2015
ER -