TY - GEN
T1 - An Indicator Based Evolutionary Algorithm for Multiparty Multiobjective Knapsack Problems
AU - Song, Zhen
AU - Luo, Wenjian
AU - Xu, Peilan
AU - Ye, Zipeng
AU - Chen, Kesheng
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024
Y1 - 2024
N2 - As a special case of the multiobjective optimization problem, the multiobjective knapsack problem (MOKP) widely exists in real-world applications. Currently, most algorithms used to solve MOKPs assume that these problems involve only one decision maker (DM). However, some complex MOKPs often involve more than one decision makers and we call such problems multiparty multiobjective knapsack problems (MPMOKPs). Existing algorithms cannot solve MPMOKPs effectively. To the best of our knowledge, there is only a little attention paid to MPMOKPs. In this paper, inspired by existing SMS-EMOA, we propose a novel indicator-based algorithm called SMS-MPEMOA to solve MPMOKPs, which aims to search solutions to satisfy all decision makers as much as possible. SMS-MPEMOA is compared with several state-of-the-art multiparty multiobjective optimization algorithms (MPMOEAs) on the benchmarks and the experimental results demonstrate that SMS-MPEMOA is very competitive.
AB - As a special case of the multiobjective optimization problem, the multiobjective knapsack problem (MOKP) widely exists in real-world applications. Currently, most algorithms used to solve MOKPs assume that these problems involve only one decision maker (DM). However, some complex MOKPs often involve more than one decision makers and we call such problems multiparty multiobjective knapsack problems (MPMOKPs). Existing algorithms cannot solve MPMOKPs effectively. To the best of our knowledge, there is only a little attention paid to MPMOKPs. In this paper, inspired by existing SMS-EMOA, we propose a novel indicator-based algorithm called SMS-MPEMOA to solve MPMOKPs, which aims to search solutions to satisfy all decision makers as much as possible. SMS-MPEMOA is compared with several state-of-the-art multiparty multiobjective optimization algorithms (MPMOEAs) on the benchmarks and the experimental results demonstrate that SMS-MPEMOA is very competitive.
KW - Multiobjective optimization
KW - evolutionary computation
KW - knapsack problem
KW - multiparty multiobjective optimization
UR - https://www.scopus.com/pages/publications/85190647131
U2 - 10.1007/978-3-031-57808-3_17
DO - 10.1007/978-3-031-57808-3_17
M3 - 会议稿件
AN - SCOPUS:85190647131
SN - 9783031578076
T3 - IFIP Advances in Information and Communication Technology
SP - 233
EP - 246
BT - Intelligent Information Processing XII - 13th IFIP TC 12 International Conference, IIP 2024, Proceedings
A2 - Shi, Zhongzhi
A2 - Torresen, Jim
A2 - Yang, Shengxiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024
Y2 - 3 May 2024 through 6 May 2024
ER -