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DOCEA/D: Dual-Operator-based Constrained many-objective Evolutionary Algorithm based on Decomposition
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AI
DOI：10.1007/s10586-022-03647-7.png)
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Abstract
En 中文
System virtualization is a key technology nowadays for reducing energy consumption and investment in server computers. However this environment frequently faces the problem of virtual machine placement (VMP). In order to solve this problem effectively, we have proposed a Dual-Operator-based Constrained many-objective Evolutionary Algorithm, DOCEA/D, which employs a Decomposition-based strategy. The algorithm uses differential evolution (DE) as an evolutionary mechanism and also employs a novel diversity maintenance (DM) technique to avoid trapping in local optima. For the purpose of validating the performance of DOCEA/D, it has been compared here with other contemporary many-objective evolutionary algorithms (MaOEAs) namely MOEA/D, NSGA-III and IBEA on three instances of VMP problem. The results on the different quality indicators clearly demonstrate the superiority of DOCEA/D over the counterpart approaches. As a further proof, we graphically show the superiority of our proposed method over the competing approaches and also demonstrate the statistical significance of our results.
Keywords:
Many-objective optimization
Differential evolution
VMP problem
Decomposition based MOEAs
Multi-operator MOEAs
DOCEA
D
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K
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