The competition focuses on Multiparty Multiobjective Optimization Problems
(MPMOPs), where multiple decision makers have conflicting objectives, as seen
in applications like UAV path planning. Despite their importance, MPMOPs remain
understudied in comparison to conventional multiobjective optimization. The
competition aims to address this gap by encouraging researchers to explore
tailored modeling approaches. The test suite comprises two parts: problems with
common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning
(BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the
first part are evaluated using Multiparty Inverted Generational Distance
(MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV)
metrics. The average algorithm ranking across all problems serves as a
performance benchmark.

该研究论文关注于多方多目标优化问题（MPMOPs），着眼于冲突目标的多个决策者，如无人机路径规划。通过鼓励研究人员探索定制建模方法，致力于填补 MPMOPs 相对传统多目标优化的研究空白。测试套件由两个部分组成：具有常见帕累托最优解的问题和未知解的两方多目标无人机路径规划（BPMO-UAVPP）问题。第一部分的优化算法使用多方反转生成距离（MPIGD）进行评估，第二部分则使用多方超体积（MPHV）指标进行评估。所有问题上的平均算法排名作为性能基准。

CEC 2024 多方多目标优化竞赛的基准

Benchmark for CEC 2024 Competition on Multiparty Multiobjective  Optimization

In the field of evolutionary multiobjective optimization, the decision maker
(DM) concerns conflicting objectives. In the real-world applications, there
usually exist more than one DM and each DM concerns parts of these objectives.
Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict
the MOP with multiple decision makers involved, where each party concerns about
certain some objectives of all. However, in the evolutionary computation field,
there is not much attention paid on MPMOPs. This paper constructs a series of
MPMOPs based on distance minimization problems (DMPs), whose Pareto optimal
solutions can be vividly visualized. To address MPMOPs, the new proposed
algorithm OptMPNDS3 uses the multiparty initializing method to initialize the
population and takes JADE2 operator to generate the offsprings. OptMPNDS3 is
compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The result
shows that OptMPNDS3 is strongly comparable to other algorithms

本文构建了基于距离最小化问题（DMPs）的一系列多方多目标优化问题（MPMOPs），提出了一种新的算法 OptMPNDS3，该算法利用多方初始化方法初始化种群，并采用 JADE2 算子生成后代，与 OptAll、OptMPNDS 和 OptMPNDS2 算法在问题套件上进行对比，结果表明 OptMPNDS3 与其他算法具有很强的可比性。