# Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization

arXiv: https://arxiv.org/abs/2402.02033 · HTML: https://mlsanity.com/paper/2402.02033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization](https://mlsanity.com/paper/2206.11526/) (2022)
- [Effects of a Hovering Unmanned Aerial Vehicle on Urban Soundscapes Perception](https://mlsanity.com/paper/1912.00087/) (2019)

## Built on by (most cited first, 1 of 1)

- [RAG/LLM Augmented Switching Driven Polymorphic Metaheuristic Framework](https://mlsanity.com/paper/2505.13808/) (2025)

## Abstract (arXiv)

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.