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.

翻译：传统多目标优化问题（MOPs）在涉及多个决策者（DMs）的场景中表现不足，而这类场景在实际应用中普遍存在。此类问题被归类为多方多目标优化问题（MPMOPs）。对于MPMOPs而言，其目标是找到一个尽可能贴近每个决策者Pareto前沿的解集。这给进化算法在搜索与选择环节带来了挑战。为更好地求解MPMOPs，本文提出了一种名为多方免疫算法（MPIA）的新方法。MPIA融合了基于不同决策者视角下个体非支配排序等级的跨方引导交叉策略，以及基于所提多方覆盖度量（MCM）的自适应激活策略。这些策略使MPIA能够激活合适的个体以进行后续操作，维持不同决策者视角下的种群多样性，并增强算法的搜索能力。为评估MPIA的性能，我们将其与普通多目标进化算法（MOEAs）及最新多方多目标优化进化算法（MPMOEAs）进行对比，通过求解合成的多方多目标问题以及涉及多个决策者的真实双方案多目标无人机路径规划（BPUAV-PP）问题。实验结果表明，MPIA优于其他算法。