no code implementations • 15 Mar 2024 • Nan Gao, Jia Li, Huaibo Huang, Zhi Zeng, Ke Shang, Shuwu Zhang, Ran He
Experimental results demonstrate the superiority of DiffMAC over state-of-the-art methods, with a high degree of generalization in real-world and heterogeneous settings.
1 code implementation • 7 Sep 2022 • Tianye Shu, Ke Shang, Hisao Ishibuchi, Yang Nan
In this study, we examine the effects of the archive size on three aspects: (i) the quality of the selected final solution set, (ii) the total computation time for the archive maintenance and the final solution set selection, and (iii) the required memory size.
no code implementations • 4 Mar 2022 • Ke Shang, WeiYu Chen, Weiduo Liao, Hisao Ishibuchi
In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization.
1 code implementation • 18 Jan 2022 • Ke Shang, Tianye Shu, Hisao Ishibuchi
The learned direction vector set can then be used in the $R_2^{\text{HVC}}$ indicator to improve its approximation quality.
1 code implementation • 18 Jan 2022 • Ke Shang, Tianye Shu, Hisao Ishibuchi, Yang Nan, Lie Meng Pang
This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite.
no code implementations • 19 Aug 2021 • WeiYu Chen, Hisao Ishibuchi, Ke Shang
Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms.
no code implementations • 20 Apr 2021 • Ke Shang, Hisao Ishibuchi, WeiYu Chen, Yang Nan, Weiduo Liao
Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization.
1 code implementation • 1 Feb 2021 • WeiYu Chen, Hisao Ishibuchi, Ke Shang
Especially, in an EMO algorithm with an unbounded external archive, subset selection is an essential post-processing procedure to select a pre-specified number of solutions as the final result.
no code implementations • 14 Dec 2020 • Hisao Ishibuchi, Lie Meng Pang, Ke Shang
The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker.
no code implementations • 17 Aug 2020 • Lie Meng Pang, Hisao Ishibuchi, Ke Shang
In the final population framework, the final population of an EMO algorithm is presented to the decision maker.
no code implementations • 27 Jul 2020 • Lie Meng Pang, Hisao Ishibuchi, Ke Shang
In this framework, which is referred to as the solution selection framework, the final population does not have to be a good solution set.
no code implementations • 4 Jul 2020 • Wei-Yu Chen, Hisao Ishibuhci, Ke Shang
Subset selection is a popular topic in recent years and a number of subset selection methods have been proposed.
no code implementations • 15 Jun 2020 • Hisao Ishibuchi, Lie Meng Pang, Ke Shang
The selection of a single final solution from the obtained solutions is assumed to be done by a human decision maker.
no code implementations • 22 Mar 2020 • Wei-Yu Chen, Hisao Ishibuchi, Ke Shang
In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems.
1 code implementation • 15 May 2018 • Ke Shang, Hisao Ishibuchi, Xizi Ni
The basic idea of the proposed method is to use different line segments only in the hypervolume contribution region for the hypervolume contribution approximation.
Optimization and Control