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Multiparty Multiobjective Optimization for Discrete Problems: A Case Study on Multistakeholder Recommendation
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AI
DOI：10.1109/TEVC.2025.3615199.png)
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Abstract
En 中文
Multiparty multiobjective 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 multiparty multiobjective 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 multiparty multiobjective evolutionary framework named MP-HCEA for discrete problems, where a multiparty 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 first 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 multiparty population, which further enhances the convergence ability of the algorithm. Finally, we apply MP-HCEA to a real application named multistakeholder recommendation as a case study. Experiments on real-world multistakeholder recommendation datasets show that the proposed MP-HCEA outperforms several representative baselines.
Keywords:
Discrete problems
multiparty multiobjective optimization
multipopulation method
multistakeholder recommendation
Journal
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