Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization

It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto dominance relation for a high-dimensional space leads diversity maintenance mecha...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 18; H. 3; S. 348 - 365
Hauptverfasser:	Li, Miqing, Yang, Shengxiang, Liu, Xiaohui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Approximation algorithms Computer science; control theory; systems Convergence Decision theory. Utility theory Density diversity Dominance Estimation Evolutionary algorithms Evolutionary multiobjective optimization Exact sciences and technology Maintenance Maintenance engineering manyobjective optimization Operational research and scientific management Operational research. Management science Optimization Pareto optimality Sample size shift-based density estimation Sociology Statistics Theoretical computing evolutionary multiobjective optimization many-objective optimization diversity shift-based density estimation Convergence Density estimation Density measurement Dominating set Pareto optimum Evolutionary algorithm Space diversity Multiobjective programming Dominance Maintenance Optimization Multidimensional analysis Preference Numerical convergence
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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