arXiv Open Access 2024

Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization

Wenjian Luo Peilan Xu Shengxiang Yang Yuhui Shi

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Abstrak

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.

Topik & Kata Kunci

cs.AI cs.NE

Penulis (4)

Wenjian Luo

Peilan Xu

Shengxiang Yang

Yuhui Shi

Format Sitasi

APA MLA BibTeX

Luo, W., Xu, P., Yang, S., Shi, Y. (2024). Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization. https://arxiv.org/abs/2402.02033

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓