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

Fan, Qinqin; Yan, Xuefeng

doi:10.1007/s00500-014-1349-y

Cited by 28 publications

(10 citation statements)

References 35 publications

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“…Nowadays, more and more techniques of chaos synchronization were proposed, such as backstepping control method (Li et al 2012;Cui et al 2014), linear error feedback control Hu et al 2015;Kole 2015), adaptive control (Zhao and Jia 2014;Chen et al 2015;Fan and Yan 2015;Jose 2015), and sliding mode control (Yang et al 2010;Kang et al 2015;Zhang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive control of two totally different chaotic systems with complicated structures by novel pragmatically adaptive control strategy

et al. 2016

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In this paper, a set of novel fuzzy adaptive control strategy is proposed to control two totally different nonlinear systems with complicated structure and different numbers of nonlinear terms. This novel adaptive control strategy is composed of three main points-(1) novel fuzzy model, proposed via Li and Ge (IEEE Trans Syst Man Cybern Part B Cybern 41(4):1015-1026, 2011), provides a set of simple and effective modeling strategy to reduce the total numbers of fuzzy rules through modeling process, and only two linear subsystems are needed; (2) novel control Lyapunov function is set as an exponential form of error states and tries to speed up the variance of states in this article; and (3) pragmatical asymptotically stability theorem: this theorem can be applied to proof that the error of parameters can achieve the original point strictly. Mathieu-Van der Pol system and quantum cellular neural networks nanosystem are used for illustrations in numerical simulation results to show the effectiveness and feasibility of the new fuzzy adaptive approach. Traditional fuzzy modeling and adaptive control method are given for further comparison.

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Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive control of two totally different chaotic systems with complicated structures by novel pragmatically adaptive control strategy

et al. 2016

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“…3. In addition, the combustion of HAC and PX will produce carbon monoxide (CO), a toxic gas, and carbon dioxide (CO2), a greenhouse gas (Fan and Yan 2015). Therefore, to obtain greater economic benefits while reducing the impact of CO2 and CO in the air, it is necessary to reduce the combustion losses of HAC and PX during the reaction process.…”

Section: Case Studymentioning

confidence: 99%

An adaptive surrogate-assisted particle swarm optimization for expensive problems

2021

Soft Comput

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To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogateassisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

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“…In this oxidation process, the combustion losses of acetic acid (HAC) and PX in the reactor are two important economic indices. In addition, the combustion of HAC and PX will produce carbon monoxide (CO), a toxic gas, and carbon dioxide (CO2), a greenhouse gas (Fan and Yan 2015). Therefore, to obtain greater economic benefits while reducing the impact of CO2 and CO in the air, it is necessary to reduce the combustion losses of HAC and PX during the reaction process.…”

Section: Mixturementioning

confidence: 99%

An Adaptive Surrogate-Assisted Particle Swarm Optimization for Expensive Problems

2021

Preprint

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To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogate-assisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

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

Cited by 28 publications

References 35 publications

Fuzzy adaptive control of two totally different chaotic systems with complicated structures by novel pragmatically adaptive control strategy

Fuzzy adaptive control of two totally different chaotic systems with complicated structures by novel pragmatically adaptive control strategy

An adaptive surrogate-assisted particle swarm optimization for expensive problems

An Adaptive Surrogate-Assisted Particle Swarm Optimization for Expensive Problems

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