[2603.27541] A Novel Immune Algorithm for Multiparty Multiobjective Optimization

Computer Science > Neural and Evolutionary Computing arXiv:2603.27541 (cs) [Submitted on 29 Mar 2026] Title:A Novel Immune Algorithm for Multiparty Multiobjective Optimization Authors:Kesheng Chen, Wenjian Luo, Qi Zhou, Yujiang liu, Peilan Xu, Yuhui Shi View a PDF of the paper titled A Novel Immune Algorithm for Multiparty Multiobjective Optimization, by Kesheng Chen and 5 other authors View PDF HTML (experimental) Abstract:Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we ...