  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.
