56 citations · 166 across the 19 of their papers we have counts for
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An Unbounded Archive-based Transfer Strategy for Dynamic Multi-Objective Optimization with a Changing Number of Objectives
Zhiyun Xiao, Ke Shang, Yajun Liu +3
Dynamic multi-objective optimization with a variable number of objectives is difficult because objective-dimensional variations may significantly change the Pareto front and degrad…
A Scalable Benchmark Test Suite for Dynamic Multi-Objective Optimization with a Changing Number of Objectives
Ke Shang, Zhiyun Xiao, Yuxuan Liu +3
Dynamic multi-objective optimization with a changing number of objectives has recently attracted increasing attention due to its relevance to real-world problems whose evaluation c…
An Efficient Evolutionary Algorithm for Few-for-Many Optimization
Ke Shang, Hisao Ishibuchi, Zexuan Zhu +1
Few-for-many (F4M) optimization, recently introduced as a novel paradigm in multi-objective optimization, aims to find a small set of solutions that effectively handle a large numb…
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…
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…
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…
Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering, as a classic method to group similar data points together, has…
Hypervolume-Optimal -Distributions on Line/Plane-based Pareto Fronts in Three Dimensions
Ke Shang, Hisao Ishibuchi, Weiyu Chen +2
Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with solutions on a Pareto…
Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Subset selection is an interesting and important topic in the field of evolutionary multi-objective optimization (EMO). Especially, in an EMO algorithm with an unbounded external a…
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
Hisao Ishibuchi, Lie Meng Pang, Ke Shang
It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by a…
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. O…
Algorithm Configurations of MOEA/D with an Unbounded External Archive
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
In the evolutionary multi-objective optimization (EMO) community, it is usually assumed that the final population is presented to the decision maker as the result of the execution…
Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets
Weiyu Chen, Hisao Ishibuhci, Ke Shang
Subset selection is a popular topic in recent years and a number of subset selection methods have been proposed. Among those methods, hypervolume subset selection is widely used. G…
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
Hisao Ishibuchi, Lie Meng Pang, Ke Shang
In general, a multi-objective optimization problem does not have a single optimal solution but a set of Pareto optimal solutions, which forms the Pareto front in the objective spac…
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the…