A New Evolutionary Approach to Multiparty Multiobjective Optimization

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.