Benchmarking Real-World Many-Objective Problems: A Problem Suite With Baseline Results

In recent decades, multi-objective evolutionary algorithms (MOEAs) have been evaluated on artificial test problems with unrealistic characteristics, leading to uncertain conclusions about their efficacy in real-world applications. To address this issue, a few benchmark test suites comprising real-wo...

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Veröffentlicht in:	IEEE access Jg. 12; S. 49275 - 49290
Hauptverfasser:	Palakonda, Vikas, Kang, Jae-Mo, Jung, Heechul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Automobiles Benchmark testing Benchmarks Complexity theory Decomposition Evolutionary algorithms Evolutionary computation many-objective optimization Multi-objective optimization Multiple objective analysis Object recognition Optimization real-world problems Rough surfaces Surface roughness test suite
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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