Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy
Authors: Mingfeng Li, Weijie Zheng, Benjamin Doerr
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results further support these theoretical findings, demonstrating that even for small values of k in both OJZJ and m OJZJ, the aging strategy significantly accelerates the SMS-EMOA algorithm compared to both the original version and the variant with the stochastic population update strategy. The rest of the paper is organized as follows. Section 2 introduces the basic concept in multi-objective optimization and presents the SMS-EMOA algorithm, including its variant with the stochastic population update strategy. Section 3 introduces our simple aging strategy and gives the basic behavior of this variant when optimizing an m-objective optimization problem. In Section 4 and Section 5, we conduct the runtime analyses of the SMS-EMOA with the aging strategy on OJZJ and m OJZJ. Section 6 presents the experiments, and finally Section 7 concludes our paper. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, National Key Laboratory of Smart Farm Technologies and Systems, International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen 2Laboratoire d Informatique (LIX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: SMS-EMOA... Algorithm 2: SMS-EMOA with the Stochastic Population Update... Algorithm 3: SMS-EMOA with the Aging Strategy |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | No | The paper utilizes the OJZJ benchmark and m OJZJ benchmark, which are theoretical constructs (functions) defined within the paper, not external datasets with specific access information. Definition 2 ([Doerr and Zheng, 2021]). Let n N and k = [1..n]. The function OJZJn,k = (f1, f2) : {0, 1}n R2 is defined by... Definition 6 ([Zheng and Doerr, 2024b]). Let m be the even number of objectives. Let the problem size n be a multiple of m/2. Let n = 2n m N and k [1..n ]. |
| Dataset Splits | No | The paper uses synthetic benchmarks (OJZJ and m OJZJ) for theoretical analysis and experiments. These benchmarks are defined mathematically and do not involve dataset splits in the traditional sense of training, validation, or test sets. |
| Hardware Specification | No | The paper describes experimental parameters like problem size (n), gap parameter (k), age limit (τ), population size (µ), and number of independent runs, but it does not specify any particular hardware (e.g., GPU/CPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper mentions various algorithms (SMS-EMOA, NSGA-II, NSGA-III) and theoretical concepts, but it does not provide specific software names with version numbers that were used for implementation or experimentation (e.g., Python 3.8, PyTorch 1.9, etc.). |
| Experiment Setup | Yes | For the bi-objective problem, we chose OJZJ as characterized in Theorem 5. We set the size of the problem n {10, 15, 20, 25, 30} and fix the gap parameter at k = 4... We set the age limit τ = µ/2 and the size of the population to 2(n 2k+4)... Each algorithm was tested with 50 independent runs... For many-objective optimization, we chose m OJZJ as in Theorem 10, and we fixed the number of objectives to m = 4... We set the problem size n {12, 16, 20, 24, 28} and gap size k = 3... The age limit τ was still set to µ/2 and the population size was set to 2 (n + 1)m/2 + 1... Each algorithm was tested with 20 independent runs... |