Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization

TL;DR

To address the paucity of standardized benchmarks for multiparty multiobjective optimization, this paper proposes a two-part benchmark suite: 11 MPMOPs with common Pareto optimal solutions derived from six BF base functions (BF1–BF6) and six BPMO-UAVPP problems with unknown optima. The evaluation framework uses $MPIGD$ for the first part and $MPHV$ for the second, providing an aggregate performance ranking across problems. The BPMO-UAVPP scenarios model two decision-makers—Efficiency and Safety—under explicit constraints and realistic test cases, while the MPMOPs E1–E11 assemble time-staged objective blocks to test multiparty sorting strategies. The benchmark is designed for reproducibility, with clear experimental settings, and is publicly hosted to support future research in tailored multiparty multiobjective optimization methods.

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.