Multiparty Multiobjective Optimization for Discrete Problems: A Case Study on Multistakeholder Recommendation
提出一个名为MP-HCEA的多方多目标进化框架，用于解决离散优化问题，通过双阶段合作机制和双搜索机制提升算法性能，并在多利益相关者推荐任务中验证了有效性。
Multi-party multi-objective optimization, which aims to find a solution set that satisfies multiple decision makers (DMs) as much as possible, has attracted the attention of researchers recently. Although multi-party multi-objective optimization is of great significance in practical applications, most existing works focus on continuous problems while paying little attention to discrete problems. To this end, we propose a multi-party multi-objective evolutionary framework named MP-HCEA for discrete problems, where a multi-party population is used to optimize all objectives of multiple DMs, and multiple single-party populations are used to respectively optimize the objectives of each DM. In MP-HCEA, a dual-phase cooperation mechanism is firstly proposed to guide the population interaction, where the weak cooperation is performed in the early phase to share offspring individuals, while the strong cooperation is performed in the later phase to share parent individuals. This dual-phase cooperation mechanism not only ensures effective information sharing between multiple populations, but also helps them to obtain high-quality solutions. In addition, a novel dual-search mechanism is proposed to guide the evolution of the multi-party population, which further enhances the convergence ability of the algorithm. Finally, we apply MP-HCEA to a real application named multi-stakeholder recommendation as a case study. Experiments on real-world multi-stakeholder recommendation datasets show that the proposed MP-HCEA outperforms several representative baselines.