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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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 49275 - 49290
Main Authors:	Palakonda, Vikas, Kang, Jae-Mo, Jung, Heechul
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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