Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System

Abstract

Patient stratification has been studied widely to tackle subtype diagnosis problems for effective treatment. Due to the dimensionality curse and poor interpretability of data, there is always a long-lasting challenge in constructing a stratification model with high diagnostic ability and good generalization. To address these problems, this paper proposes two novel evolutionary multiobjective clustering algorithms with ensemble (NSGA-II-ECFE and MOEA/D-ECFE) with four cluster validity indices used as the objective functions. First, an effective ensemble construction method is developed to enrich the ensemble diversity. After that, an ensemble clustering fitness evaluation (ECFE) method is proposed to evaluate the ensembles by measuring the consensus clustering under those four objective functions. To generate the consensus clustering, ECFE exploits the hybrid co-association matrix from the ensembles and then dynamically selects the suitable clustering algorithm on that matrix. Multiple experiments have been conducted to demonstrate the effectiveness of the proposed algorithm in comparison with seven clustering algorithms, twelve ensemble clustering approaches, and two multiobjective clustering algorithms on 55 synthetic datasets and 35 real patient stratification datasets. The experimental results demonstrate the competitive edges of the proposed algorithms over those compared methods. Furthermore, the proposed algorithm is applied to extend its advantages by identifying cancer subtypes from five cancer-related single-cell RNA-seq datasets.

Dimensionality Reduction, Modelling, and Optimization of Multivariate Problems Based on Machine Learning

“…Due to the efficiency of these dimensionality reduction techniques, active subspaces can be used and studied in some engineering and mathematical problems [4,8,[15][16][17]. Recently, many studies have been proposed to tackle the real world and industrial problems such as the optimization of the industrial copper burdening system [18], the surrogate model for low Reynolds number airfoil based on transfer learning [19], and parameter reduction of composite load model proposed by the Western Electricity Coordinating Council [20]. Moreover, In the research proposed by Wang et al [21], twenty-two dimensional functions related to airfoil manufacturing errors were approximated through the response surface in the one-dimensional active subspace.…”

Multiple Attention Mechanism Graph Convolution HAR Model Based on Coordination Theory

“…With the rapid development of machine learning and deep learning, many end-to-end motion recognition algorithms have appeared. These methods do not need to consume a lot of manpower and can achieve high recognition accuracy [ 8 , 9 ].…”

A Novel Cooperation Multi-Objective Optimization Approach: Multi-Swarm Multi-Objective Evolutionary Algorithm Based on Decomposition (MSMOEA/D)

“…With MOEAs, multiple models can be obtained in one optimization, not just a single solution. Therefore, it is widely used in radio frequency identification (Ma et al, 2021c;Ma et al, 2021d), feature selection (Karagoz et al, 2020), and structure optimization (Wang et al, 2020) and other fields.…”