Enhancing Multi-Objective Optimization with Automatic Construction of Parallel Algorithm Portfolios

It has been widely observed that there exists no universal best Multi-Objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-Objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs ind...

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Veröffentlicht in:	Electronics (Basel) Jg. 12; H. 22; S. 4639
Hauptverfasser:	Ma, Xiasheng, Liu, Shengcai, Hong, Wenjing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2023
Schlagworte:	Algorithms Analysis Business metrics Design Evolution & development Evolutionary algorithms Genetic algorithms Investment analysis Multiple objective analysis Mutation Optimization Performance evaluation Traveling salesman problem Variables Germany
ISSN:	2079-9292, 2079-9292
Online-Zugang:	Volltext
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