A Nearest Prototype Selection Algorithm Using Multi-objective Optimization and Partition

2013 ◽  
Author(s):  
Juan Li ◽  
Yuping Wang
Keyword(s):  
Prototype Selection ◽  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Multi Objective

Multi-Objective Optimization-Based Networked Multi-Label Active Learning

2019 ◽  
Vol 30 (2) ◽  
pp. 1-26
Author(s):  
Lei Li ◽  
Yuqi Chu ◽  
Guanfeng Liu ◽  
Xindong Wu
Keyword(s):  
Prediction Accuracy ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Research Directions ◽  
Seed Node ◽  
Multi Objective ◽  
Network Applications ◽  
Fast Development ◽  
System Overhead

Along with the fast development of network applications, network research has attracted more and more attention, where one of the most important research directions is networked multi-label classification. Based on it, unknown labels of nodes can be inferred by known labels of nodes in the neighborhood. As both the scale and complexity of networks are increasing, the problems of previously neglected system overhead are turning more and more seriously. In this article, a novel multi-objective optimization-based networked multi-label seed node selection algorithm (named as MOSS) is proposed to improve both the prediction accuracy for unknown labels of nodes from labels of seed nodes during classification and the system overhead for mining the labels of seed nodes with third parties before classification. Compared with other algorithms on several real networked data sets, MOSS algorithm not only greatly reduces the system overhead before classification but also improves the prediction accuracy during classification.

The Application of an Immune Clonal Selection Algorithm Based on the Information Entropy in Truss Structure Multi-Objective Optimization

2012 ◽  
Vol 249-250 ◽  
pp. 1119-1125
Author(s):  
Chang Yuan Hu ◽  
He Sheng Tang ◽  
Li Xin Deng ◽  
Song Tao Xue
Keyword(s):  
Information Entropy ◽  
Optimization Design ◽  
Clonal Selection ◽  
Minimum Weight ◽  
Truss Structures ◽  
Penalty Function Method ◽  
Clonal Selection Algorithm ◽  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Multi Objective

In order to solve the conflict multi-objective optimization of truss structures between the structure minimum weight and safety redundancy, the immune clonal selection algorithm based on information entropy was adopted in this paper. Based on the immunology theory, the non-dominated neighbor-based selection, proportional cloning and elitism strategy were introduced in the multi-objective immune clonal selection algorithm (MOICSA) to enhance the diversity, the uniformity and the convergence of the obtained solution. Mathematical models for truss multi-objective optimization design are constructed, in which the information entropy value of bar stress is taken as one of objective functions, and penalty function method was used to deal with violated constraints. Several classical problems are solved using the MOICSA algorithm, and the results compared with other optimization methods. The simulation results show that the method can achieve the effect of multiple-objective optimization successfully.

Clone Selection Algorithm to Solve Preference Multi-Objective Optimization

2010 ◽  
Vol 21 (1) ◽  
pp. 14-33 ◽  
Author(s):  
Dong-Dong YANG ◽  
Li-Cheng JIAO ◽  
Mao-Guo GONG ◽  
Hang YU
Keyword(s):  
Multi Objective Optimization ◽  
Selection Algorithm ◽  
Multi Objective ◽  
Clone Selection
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