316 citations · 1.3k across the 49 of their papers we have counts for
7 papers · 1 filter
Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization
Tianye Shu, Ke Shang, Hisao Ishibuchi +1
An unbounded external archive has been used to store all nondominated solutions found by an evolutionary multi-objective optimization algorithm in some studies. It has been shown t…
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
Wei Liu, Rui Wang, Tao Zhang +3
Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades. This study seeks to solve MO…
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Takato Kinoshita, Naoki Masuyama, Yiping Liu +2
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimizat…
HV-Net: Hypervolume Approximation based on DeepSets
Ke Shang, Weiyu Chen, Weiduo Liao +1
In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization. The basic idea of HV-Net is to use DeepSets, a deep neur…
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation
Ke Shang, Tianye Shu, Hisao Ishibuchi
Hypervolume contribution is an important concept in evolutionary multi-objective optimization (EMO). It involves in hypervolume-based EMO algorithms and hypervolume subset selectio…
Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning
Naoki Masuyama, Narito Amako, Yuna Yamada +2
Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general…
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Ke Shang, Tianye Shu, Hisao Ishibuchi +2
In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown…