TY - GEN
T1 - A Clustering-Based Multiobjective Evolutionary Algorithm for Balancing Exploration and Exploitation
AU - Zheng, Wei
AU - Wu, Jianyu
AU - Zhang, Chenghu
AU - Sun, Jianyong
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - This paper proposes a simple but promising clustering-based multi-objective evolutionary algorithm, termed as CMOEA. At each generation, CMOEA first divides the current population into several subpopulations by Gaussian mixture clustering. To generate offsprings, the search stage, either exploration or exploitation, is determined by the relative difference between the subpopulations’ hypervolumes of two adjacent generations. CMOEA selects the parents from different subpopulations in case of exploration stage, and from the same subpopulation in case of exploitation stage. In the environmental selection phase, the hypervolume indicator is used to update the population. Simulation experiments on nine multi-objective problems show that CMOEA is competitive with five popular multi-objective evolutionary algorithms.
AB - This paper proposes a simple but promising clustering-based multi-objective evolutionary algorithm, termed as CMOEA. At each generation, CMOEA first divides the current population into several subpopulations by Gaussian mixture clustering. To generate offsprings, the search stage, either exploration or exploitation, is determined by the relative difference between the subpopulations’ hypervolumes of two adjacent generations. CMOEA selects the parents from different subpopulations in case of exploration stage, and from the same subpopulation in case of exploitation stage. In the environmental selection phase, the hypervolume indicator is used to update the population. Simulation experiments on nine multi-objective problems show that CMOEA is competitive with five popular multi-objective evolutionary algorithms.
KW - Exploitation
KW - Exploration
KW - Gaussian mixture clustering method
KW - Multi-objective evolutionary algorithm
UR - https://www.scopus.com/pages/publications/85083995842
U2 - 10.1007/978-981-15-3425-6_28
DO - 10.1007/978-981-15-3425-6_28
M3 - 会议稿件
AN - SCOPUS:85083995842
SN - 9789811534249
T3 - Communications in Computer and Information Science
SP - 355
EP - 369
BT - Bio-inspired Computing
A2 - Pan, Linqiang
A2 - Liang, Jing
A2 - Qu, Boyang
PB - Springer
T2 - 14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019
Y2 - 22 November 2019 through 25 November 2019
ER -