Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms

One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is that optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing "experiences" to constru...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 22; H. 4; S. 501 - 514
Hauptverfasser:	Jiang, Min, Huang, Zhongqiang, Qiu, Liming, Huang, Wenzhen, Yen, Gary G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Classification Dimensionality reduction domain adaption dynamic multiobjective optimization evolutionary algorithm (EA) Evolutionary algorithms Genetic algorithms Heuristic algorithms Machine learning Mathematical models Multiple objective analysis Optimization Pareto optimization Particle swarm optimization Population (statistical) Prediction algorithms Predictive models Sociology Sorting algorithms Statistics transfer learning
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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