Summary of Benchmark For Cec 2024 Competition on Multiparty Multiobjective Optimization, by Wenjian Luo et al.
Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
by Wenjian Luo, Peilan Xu, Shengxiang Yang, Yuhui Shi
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research paper presents a competition focused on solving Multiparty Multiobjective Optimization Problems (MPMOPs). MPMOPs involve multiple decision makers with conflicting objectives, which is crucial in applications such as unmanned aerial vehicle (UAV) path planning. Despite their importance, MPMOPs have received limited attention compared to conventional multiobjective optimization problems. The competition aims to address this gap by encouraging researchers to develop tailored modeling approaches for these complex problems. The test suite consists of two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. To evaluate the performance of optimization algorithms, the paper uses Multiparty Inverted Generational Distance (MPIGD) and Multiparty Hypervolume (MPHV) metrics for the first and second parts, respectively. The average algorithm ranking across all problems serves as a benchmark to assess the effectiveness of different approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research competition is about solving complex problems where multiple people want different things. This is important in areas like guiding drones safely. Currently, there’s not enough work on these kinds of problems. The competition wants to change this by encouraging experts to develop new ways to solve them. The test problems have two types: some with known good solutions and others that are completely unknown. To see how well different methods do, the researchers will use special tools to measure their performance. |
Keywords
» Artificial intelligence » Attention » Optimization
