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A Multi-Objective Evolutionary Algorithm Based on Principal Component Analysis and Grid Division

Authors: Mei Ma; Hecheng Li; Jing Huang;

A Multi-Objective Evolutionary Algorithm Based on Principal Component Analysis and Grid Division

Abstract

In order to effectively solve the many objective optimization problems (MaOPs), a new multi-objective evolutionary algorithm is proposed. Firstly, utilizes the principal component analysis (PCA) to reduce the dimension of objective space. The main idea is do a correlation analysis between objectives. Secondly, puts forward a new grid division method. In the course of dividing, make sure all grid sizes are equal. The simulation illustrates the efficiency of the proposed algorithm.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
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