针对多方决策优化难题，提出新型免疫算法提升解集质量与多样性。
A Novel Immune Algorithm for Multiparty Multiobjective Optimization
- 引入跨方引导交叉策略，融合多方非支配排序信息。
- 基于多方覆盖度量实现个体自适应激活，保持多视角多样性。
- 在合成与真实无人机路径规划问题上优于现有算法。
传统多目标优化问题（MOPs）难以应对多决策者（DMs）场景，此类问题被归为多方多目标优化问题（MPMOPs）。MPMOPs的目标是找到尽可能接近每位决策者帕累托前沿的解集，这对进化算法的搜索与选择能力构成挑战。为此，本文提出一种新型多主体免疫算法（MPIA），包含基于各决策者视角下个体非支配排序等级的跨方引导交叉策略，以及基于新提出的多主体覆盖度量（MCM）的自适应激活策略。这些策略使算法能有效激活合适个体参与后续操作，从多方视角维持种群多样性，并增强搜索能力。通过求解合成多方多目标问题及涉及多个决策者的实际双边无人机路径规划（BPUAV-PP）问题，对MPIA进行评估。实验结果表明，MPIA在性能上优于普通多目标进化算法（MOEAs）和当前最先进的多方多目标进化算法（MPMOEAs）。
原文摘要 · Abstract (English)
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 compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multiparty multiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.
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