Multi-Party Multi-Objective Optimization as Consensus Search: Runtime Analysis of Cross-Party Recombination
The paper discusses multi-party multi-objective optimization problems (MPMOPs) and their unique requirements for consensus among decision makers. It presents a runtime analysis of cross-party recombination, highlighting the differences from traditional single-party approaches. The authors demonstrate that their proposed methods can achieve better efficiency in finding common Pareto-optimal solutions.
- ▪The study focuses on the challenges of consensus search in multi-party multi-objective optimization problems.
- ▪It proves that a specific mutation baseline requires a significant number of fitness evaluations due to gap-crossing bottlenecks.
- ▪An analytical variant of CPR-NSGA-II is shown to discover common Pareto-optimal solutions more efficiently than traditional methods.
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Computer Science > Artificial Intelligence arXiv:2605.17454 (cs) [Submitted on 17 May 2026] Title:Multi-Party Multi-Objective Optimization as Consensus Search: Runtime Analysis of Cross-Party Recombination Authors:Xiaolei Fang, Peilan Xu, Wenjian Luo View a PDF of the paper titled Multi-Party Multi-Objective Optimization as Consensus Search: Runtime Analysis of Cross-Party Recombination, by Xiaolei Fang and 2 other authors View PDF HTML (experimental) Abstract:Multi-party multi-objective optimization problems (MPMOPs) require consensus among autonomous decision makers and therefore differ from flattened many-objective formulations.
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