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An empirical study on parameter selection for multiobjective optimization algorithms using Differential Evolution

Authors: Gilberto Reynoso-Meza; Javier Sanchis; Xavier Blasco; Miguel Martinez;

An empirical study on parameter selection for multiobjective optimization algorithms using Differential Evolution

Abstract

In this work, an empirical study on multiobjective optimization algorithms based on Differential Evolution (DE) algorithm is performed. The study focuses on getting good initial choices for evolutionary parameters for DE algorithm to tackle the multiobjective problem of PI controller tuning. This is an important issue in the field of control engineering, because the PI-PID controller remains a reliable digital solution for industrial processes. This study will bring an insight to the capabilities of a basic DE algorithm for multiobjective optimization, which can be the basis for more complex approaches using this evolutionary technique.

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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!
6
Average
Average
Average
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