5 papers
Random is Faster than Systematic in Multi-Objective Local Search
Zimin Liang, Miqing Li
Local search is a fundamental method in operations research and combinatorial optimisation. It has been widely applied to a variety of challenging problems, including multi-objecti…
Not Just for Archiving: Provable Benefits of Reusing the Archive in Evolutionary Multi-objective Optimization
Shengjie Ren, Zimin Liang, Miqing Li +1
Evolutionary Algorithms (EAs) have become the most popular tool for solving widely-existed multi-objective optimization problems. In Multi-Objective EAs (MOEAs), there is increasin…
When to Truncate the Archive? On the Effect of the Truncation Frequency in Multi-Objective Optimisation
Zhiji Cui, Zimin Liang, Lie Meng Pang +2
Using an archive to store nondominated solutions found during the search of a multi-objective evolutionary algorithm (MOEA) is a useful practice. However, as nondominated solutions…
On the Problem Characteristics of Multi-objective Pseudo-Boolean Functions in Runtime Analysis
Zimin Liang, Miqing Li
Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research…
A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization
Shengjie Ren, Zimin Liang, Miqing Li +1
Evolutionary algorithms (EAs) have been widely applied to multi-objective optimization due to their population-based nature. Population update, a key component in multi-objective E…