Boundary Decomposition for Nadir Objective Vector Estimation
Authors: Ruihao Zheng, Zhenkun Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement BDNE for black-box MOPs and use 28 black-box problems to validate its performance. The results indicate that BDNE remarkably outperforms the existing methods. |
| Researcher Affiliation | Academia | Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing, Southern University of Science and Technology 12132686@mail.sustech.edu.cn, wangzhenkun90@gmail.com |
| Pseudocode | Yes | Algorithm 1 BDNE |
| Open Source Code | Yes | The source code is available at https://github.com/Eric Zheng1024/BDNE. |
| Open Datasets | Yes | Moreover, we select problems with different shapes of feasible objective regions, including 6 existing test problems [46, 47, 48, 49] (DTLZ3, m DTLZ3, Ma F2, DTLZ5, IMOP4, and IMOP6) and 4 real-world problems [50, 51, 52] (MP-DMP, ML-DMP, RE3-4-7, and RE5-3-1). |
| Dataset Splits | No | The paper does not explicitly provide percentages or counts for training, validation, and test dataset splits. The datasets used are multi-objective optimization problems, which are typically evaluated as a whole rather than with predefined splits for training, validation, and testing like in supervised learning. |
| Hardware Specification | Yes | Experiments are executed on a computer equipped with two 3.00-GHz Intel Xeon Gold 6248R CPUs (48 cores in total), an NVIDIA T400 GPU, and 128GB of RAM. |
| Software Dependencies | No | Our codes rely on the MATLAB-based platform called Plat EMO [59], which is freely available for research purposes (see https://github.com/BIMK/Plat EMO). |
| Experiment Setup | Yes | For BDNE, we set µ = 100 and τl = 200. Then τu = 14 according to the setting of τl and FEmax. The general algorithm settings are summarized in Table 2, where FEmax means maximum number of function evaluations. Table 2: General algorithm settings. Symbol Description τu(τl) Maximum number of iterations for each CMA-ES procedure (the MOEA solving LLOPs). N N m Z; number of generated LLOPs in each iteration of m ULOPs. P |P| = N; population of the MOEA for solving N LLOPs. A |A| = N; elite archive preserved the current best solutions for N LLOPs. wi,j The j-th boundary weight vector of the i-th objective. m N FEmax Operator 3 60 180,000 SBX+PM [53] 5 100 300,000 8 160 480,000 |