An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions
Somereal-world optimization problems involve multiple decision makers holding different positions, each of whom has multiple conﬂicting objectives. These problems are deﬁned as multiparty multiobjective optimization problems (MPMOPs). Although evolutionary multiobjective optimization has been widely studied for many years, little attention has been paid to multiparty multiobjective optimization in the ﬁeld of evolutionary computation. In this paper, a class of MPMOPs, that is, MPMOPs having common Pareto optimal solutions, is addressed. A benchmark for MPMOPs, obtained by modifying an existing dynamic multiobjective optimization benchmark, is provided, and a multiparty multiobjective evolutionary algorithm to ﬁnd the common Pareto optimal set is proposed. The results of experiments conducted using the benchmark show that the proposed multiparty multiobjective evolutionary algorithm is effective.
The file attached to this record is the author's final peer reviewed version.
Citation : Liu, W. Luo, W., Lin, X., Li, M. and Yang, S. (2020) An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions. Proceedings of the 2020 IEEE Congress on Evolutionary Computation, Glasgow, UK, July 2020, (in press).
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes