Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization

Author:

Wenjian Luo, Peilan Xu, Shengxiang Yang, Yuhui Shi

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE)

journal:

date:

2024-02-03 00:00:00

Abstract

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.

PDF: Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization.pdf