8 papers
On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
Menghao Tang, Zimin Liang, Miqing Li
Scalability of evolutionary algorithms refers to assessing how their performance changes as problem size increases. In the area of multi-objective optimisation, research on the sca…
Variable Search Stepsize for Randomized Local Search in Multi-Objective Combinatorial Optimization
Xuepeng Ren, Maocai Wang, Guangming Dai +4
Over the past two decades, research in evolutionary multi-objective optimization has predominantly focused on continuous domains, with comparatively limited attention given to mult…
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…
Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results
Zimin Liang, Miqing Li, Per Kristian Lehre
Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update sinc…