An Improved S-Metric Selection Evolutionary Multi-Objective Algorithm With Adaptive Resource Allocation

One of the main disadvantages of evolutionary multi-objective algorithms (EMOAs) based on hypervolume is the computational cost of the hypervolume computation. This deficiency gets worse either when an EMOA calculates the hypervolume several times or when it is dealing with problems having more than...

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Veröffentlicht in:	IEEE access Jg. 6; S. 63382 - 63401
Hauptverfasser:	Menchaca-Mendez, Adriana, Montero, Elizabeth, Zapotecas-Martinez, Saul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Benchmark testing Computational efficiency Computing costs Evolutionary algorithms Evolutionary multi-objective algorithms Geometry hypervolume indicator indicator-based EMOAs Iterative methods Linear programming Multiple objective analysis Pareto optimization Problem solving Resource allocation Resource management
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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