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Found 36 papers, 10 papers with code
Data-Driven Preference Sampling for Pareto Front Learning
In this manner, the proposed method can sample preference vectors from the location of the Pareto front with a high probability.
Evolutionary Preference Sampling for Pareto Set Learning
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network.
Improving Critical Node Detection Using Neural Network-based Initialization in a Genetic Algorithm
The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network.
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
In the clustering domain, various algorithms with a federated learning framework (i. e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy.
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
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 clustering performance.
Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization
In this study, we examine the effects of the archive size on three aspects: (i) the quality of the selected final solution set, (ii) the total computation time for the archive maintenance and the final solution set selection, and (iii) the required memory size.
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades.
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs).
HV-Net: Hypervolume Approximation based on DeepSets
In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization.
Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning
In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm.
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation
The learned direction vector set can then be used in the $R_2^{\text{HVC}}$ indicator to improve its approximation quality.
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite.
Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization
Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms.
On fine-tuning of Autoencoders for Fuzzy rule classifiers
Recent discoveries in Deep Neural Networks are allowing researchers to tackle some very complex problems such as image classification and audio classification, with improved theoretical and empirical justifications.
Hypervolume-Optimal $μ$-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions
Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization.
Multi-label Classification via Adaptive Resonance Theory-based Clustering
This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation.
Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Especially, in an EMO algorithm with an unbounded external archive, subset selection is an essential post-processing procedure to select a pre-specified number of solutions as the final result.
Niching Diversity Estimation for Multi-modal Multi-objective Optimization
Since equivalent solutions are overlapping (i. e., occupying the same position) in the objective space, standard diversity estimators such as crowding distance are likely to select one of them and discard the others, which may cause diversity loss in the decision space.
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker.
An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios
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 evolutionary algorithms (MOEAs) in recent studies.
Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
To address this issue, we present a review of the existing configurations of the DE mutation operator in MOEA/D-DE and systematically examine the influence of each component on the performance of MOEA/D-DE.
A Niching Indicator-Based Multi-modal Many-objective Optimizer
Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions.
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms
The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets.
Non-elitist Evolutionary Multi-objective Optimizers Revisited
Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs.
A Review of Evolutionary Multi-modal Multi-objective Optimization
Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space.
An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space
One promising approach for understanding quality indicators is the use of the optimal distribution of objective vectors that optimizes each quality indicator.
An Easy-to-use Real-world Multi-objective Optimization Problem Suite
Although synthetic test problems are widely used for the performance assessment of evolutionary multi-objective optimization algorithms, they are likely to include unrealistic properties which may lead to overestimation/underestimation.
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
In the final population framework, the final population of an EMO algorithm is presented to the decision maker.
Algorithm Configurations of MOEA/D with an Unbounded External Archive
In this framework, which is referred to as the solution selection framework, the final population does not have to be a good solution set.
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
The selection of a single final solution from the obtained solutions is assumed to be done by a human decision maker.
A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm
With a clearing mechanism and a greedy removal strategy, our proposed algorithm can effectively preserve equivalent Pareto optimal solutions (i. e., different Pareto optimal solutions with same objective values).
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems.
R2-based Hypervolume Contribution Approximation
The basic idea of the proposed method is to use different line segments only in the hypervolume contribution region for the hypervolume contribution approximation.
Optimization and Control
Soft Computing Techniques for Dependable Cyber-Physical Systems
Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
Transfer Prototype-based Fuzzy Clustering
Specifically, the idea of leveraging knowledge from the source domain is exploited to develop a set of transfer prototype based fuzzy clustering (TPFC) algorithms.
