no code implementations • 5 Nov 2022 • Ke Li, Renzhi Chen, Xin Yao
Many real-world problems are usually computationally costly and the objective functions evolve over time.
no code implementations • 28 May 2022 • Renzhi Chen, Ke Li
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions.
no code implementations • 12 Sep 2021 • Ke Li, Renzhi Chen
Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model.
no code implementations • 2 Jan 2018 • Ke Li, Renzhi Chen, Dragan Savic, Xin Yao
In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i. e., a set of reference points that are biased toward to the ROI.
no code implementations • 21 Nov 2017 • Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao
When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously.
no code implementations • 23 Aug 2016 • Renzhi Chen, Ke Li, Xin Yao
Existing studies on dynamic multi-objective optimization focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature.