23 citations · 33 across the 8 of their papers we have counts for
6 papers · 1 filter
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Ke Li, Renzhi Chen, Xin Yao
Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been…
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
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 function…
Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation
Ke Li, Renzhi Chen
Multi-objective optimization problems are ubiquitous in real-world science, engineering and design optimization problems. It is not uncommon that the objective functions are as a b…
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
Ke Li, Renzhi Chen, Dragan Savic +1
Decomposition has become an increasingly popular technique for evolutionary multi-objective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approxima…
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
Ke Li, Renzhi Chen, Guangtao Fu +1
When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, t…
Dynamic Multi-Objectives Optimization with a Changing Number of Objectives
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…