TY - GEN
T1 - A New Evolutionary Approach to Multiparty Multiobjective Optimization
AU - She, Zeneng
AU - Luo, Wenjian
AU - Chang, Yatong
AU - Lin, Xin
AU - Tan, Ying
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Multiparty multiobjective optimization problems (MPMOPs) are a type of multiobjective optimization problems (MOPs), where multiple decision makers are involved, different decision makers have different objectives to optimize, and at least one decision maker has more than one objective. Although evolutionary multiobjective optimization has been studied for many years in the evolutionary computation field, evolutionary multiparty multiobjective optimization has not been paid much attention. To address the MPMOPs, the algorithm based on a multiobjective evolutionary algorithm is proposed in this paper, where the non-dominated levels from multiple parties are regarded as multiple objectives to sort the candidates in the population. Experiments on the benchmark that have common Pareto optimal solutions are conducted in this paper, and experimental results demonstrate that the proposed algorithm has a competitive performance.
AB - Multiparty multiobjective optimization problems (MPMOPs) are a type of multiobjective optimization problems (MOPs), where multiple decision makers are involved, different decision makers have different objectives to optimize, and at least one decision maker has more than one objective. Although evolutionary multiobjective optimization has been studied for many years in the evolutionary computation field, evolutionary multiparty multiobjective optimization has not been paid much attention. To address the MPMOPs, the algorithm based on a multiobjective evolutionary algorithm is proposed in this paper, where the non-dominated levels from multiple parties are regarded as multiple objectives to sort the candidates in the population. Experiments on the benchmark that have common Pareto optimal solutions are conducted in this paper, and experimental results demonstrate that the proposed algorithm has a competitive performance.
KW - Evolutionary computation
KW - Multiobjective optimization
KW - Multiparty multiobjective optimization
UR - https://www.scopus.com/pages/publications/85112063670
U2 - 10.1007/978-3-030-78811-7_6
DO - 10.1007/978-3-030-78811-7_6
M3 - 会议稿件
AN - SCOPUS:85112063670
SN - 9783030788100
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 58
EP - 69
BT - Advances in Swarm Intelligence - 12th International Conference, ICSI 2021, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Advances in Swarm Intelligence, ICSI 2021
Y2 - 17 July 2021 through 21 July 2021
ER -