316 citations · 1.3k across the 49 of their papers we have counts for
34 papers · 1 filter
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…
Optimal Distribution of Solutions for Crowding Distance on Linear Pareto Fronts of Two-Objective Optimization Problems
Hisao Ishibuchi, Lie Meng Pang
Characteristics of an evolutionary multi-objective optimization (EMO) algorithm can be explained using its best solution set. For example, the best solution set for SMS-EMOA is the…
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…
Large Language Model-Based Benchmarking Experiment Settings for Evolutionary Multi-Objective Optimization
Lie Meng Pang, Hisao Ishibuchi
When we manually design an evolutionary optimization algorithm, we implicitly or explicitly assume a set of target optimization problems. In the case of automated algorithm design,…
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects
Javier Poyatos, Javier Del Ser, Salvador Garcia +6
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems de…
Evolutionary Preference Sampling for Pareto Set Learning
Rongguang Ye, Longcan Chen, Jinyuan Zhang +1
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network. PSL employs preference vectors to scalarize multiple objectives, fa…
Improving Critical Node Detection Using Neural Network-based Initialization in a Genetic Algorithm
Chanjuan Liu, Shike Ge, Zhihan Chen +4
The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network. These nodes play a significant role in maintaining the connectivity of the ne…
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima +3
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on cluster…
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…
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…
Niching Diversity Estimation for Multi-modal Multi-objective Optimization
Yiming Peng, Hisao Ishibuchi
Niching is an important and widely used technique in evolutionary multi-objective optimization. Its applications mainly focus on maintaining diversity and avoiding early convergenc…
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…
An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios
Ryoji Tanabe, Hisao Ishibuchi
An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evol…
Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
Ryoji Tanabe, Hisao Ishibuchi
A decomposition-based multi-objective evolutionary algorithm with a differential evolution variation operator (MOEA/D-DE) shows high performance on challenging multi-objective prob…
A Niching Indicator-Based Multi-modal Many-objective Optimizer
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective…
Non-elitist Evolutionary Multi-objective Optimizers Revisited
Ryoji Tanabe, Hisao Ishibuchi
Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the per…
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good…