NDLI: Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization

Content Provider	Springer Nature Link
Author	Fan, Qinqin Yan, Xuefeng
Copyright Year	2014
Abstract	Considering that the model of the $$p$$ -Xylene (PX) oxidation reaction process is a hybrid and highly nonlinear model, a differential evolution algorithm with self-adaptive mutation strategy and control parameters (SSCPDE) was proposed to optimize the operating conditions. In SSCPDE, each individual has its own control parameters and mutation strategies that can be self-adaptively adjusted to different evolution phases and various optimization problems. SSCPDE was compared with 6 state-of-the-art DE variants by 38 different types of benchmark functions. Simulation results show that the average performance of SSCPDE is better than the six famous self-adaptive DE algorithms. Finally, the SSCPDE algorithm was used to optimize the five main operating conditions of the PX oxidation reaction process. Optimization results indicate that the production cost, loss of acetic acid and PX combustion of the PX oxidation reaction process are greatly reduced and that SSCPDE performs better than JADE, EPSDE, SaDE, and the optimizer of Aspen Plus and similar to jDE and CoDE.
Starting Page	1363
Ending Page	1391
Page Count	29
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	19
Issue Number	5
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-07-29
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Process optimization $$p$$ -Xylene oxidation process Differential evolution algorithm Parameter adaptation Mutation strategy adaptation Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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