An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

open access articleIn dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions

Review on Prescription Compatibility of Shaoyao Gancao Decoction and Reflection on Pharmacokinetic Compatibility Mechanism of Traditional Chinese Medicine Prescription Based on In Vivo Drug Interaction of Main Efficacious Components

Shaoyao Gancao Decoction (SGD) derived from Zhang Zhongjing’s “Typhoid Theory” is composed of peony and licorice, having the efficacy of nourishing liver, relaxing spasm, and relieving pain. Modern compatibility studies of SGD on chemistry, pharmacology, and pharmacokinetics all demonstrate the reasonable compatibility of peony and licorice. However, the present research on pharmacokinetics is only descriptive and limited to the influence on in vivo dynamic process of certain ingredients; correspondingly, there is lack of studies on the essence of these efficacious substances’ in vivo changes; that is, whether it is because there exists in vivo drug interaction in absorption, distribution, metabolism, and excretion (ADME) of active ingredients that leads to the improvement of bioavailability. We herein take SGD as an example and suggest that it is necessary to study in vivo drug interaction of main efficacious components mediated by metabolic enzymes, transport proteins, or plasma protein binding in the course of ADME, which is helpful to illustrate the principle of pharmacokinetic compatibility from the essence leading to the changes of effective substances in vivo

Event-triggered cooperative control for high-order nonlinear multi-agent systems with finite-time consensus

An event-triggered adaptive control algorithm is proposed for cooperative tracking control of high-order nonlinear multiagent systems (MASs) with prescribed performance and full-state constraints. The algorithm combines dynamic surface technology and the backstepping recursive design method, with radial basis function neural networks (RBFNNs) used to approximate the unknown nonlinearity. The barrier Lyapunov function and finite-time stability theory are employed to prove that all agent states are semi-globally uniform and ultimately bounded, with the tracking error converging to a bounded neighborhood of zero in a finite time. Numerical simulations are provided to demonstrate the effectiveness of the proposed control scheme

An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

Chen M, Guo Y, Jin Y, Yang S, Gong D, Yu Z. An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems. Complex & Intelligent Systems. 2023;9(1):659-675.In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions

The transcriptome expression levels related to ovulation induction and acupuncture protection therapy in rats through gene microarray

This study aimed to explore the potential molecular mechanisms of acupuncture in the adjuvant treatment of infertility. A rat model of ovulation induction was constructed using horse serum gonadotropin and human chorionic gonadotropin, and then acupuncture was used to treat the model rats at Guanyuan and Sanyinjiao points. Hematoxylin eosin (HE) staining, the endometrial thickness, and enlargement of uterine cavity were determined to evaluate the effects of acupuncture on the uterus of ovulation induction rats. Meanwhile, the uterus tissues were sent for gene microarray analyses. Acupuncture enhanced the endometrial receptivity of ovulation induction rats. Gene microarray showed that 189 overlapped differential expressed genes (DEGs) were identified, and these overlapped DEGs were divided into four clusters by Mfuzz algorithm. Afterwards, interaction networks containing 842 interaction pairs were established, and the DEGs in the interaction networks were significantly enriched in 20 BP and 10 KEGG pathways, including wound healing, response to glucocorticoid, TNF signaling pathway, terpenoid backbone biosynthesis, and glutathione metabolism. By calculating topological parameters, Anxa5, Casp8, Tgm2, Frk, Mmp7, Timp1, Hmgcr, Cth, Serpinal, and Abcbla were the important hub genes of the interaction networks. Our findings revealed the changes at transcriptome levels related to ovulation induction and acupuncture protection therapy

The Herb-Drug Interaction of Clopidogrel and Xuesaitong Dispersible Tablet by Modulation of the Pharmacodynamics and Liver Carboxylesterase 1A Metabolism

Objective. Clopidogrel and Xuesaitong dispersible tablet (XST) have been clinically proven to be effective for treating cardiocerebrovascular disease. The present study was to investigate the herb-drug interaction of Clopidogrel and XST by modulation of the pharmacodynamics and liver Carboxylesterase 1A(CES1A) metabolism. Methods. 30 male SD rats were randomly divided into a control group (equal volumes of saline, 6 rats for mRNA analysis), a clopidogrel group (clopidogrel with dose 30 mg/kg), and a combination group (clopidogrel and XST, with dose 30 and 50 mg/kg respectively, each group continuous administration once daily for 30 days). The clopidogrel and combination group comprised 12 rats, with 6 designated for mRNA analysis and 6 for the pharmacokinetic study. The 2-bromo-3’-methoxyacetophenone- (MPB-) derivatized clopidogrel active thiol metabolite (CAMD) was measured by UHPLC-MS/MS for pharmacokinetics (n=6). The expression of CES1A mRNA was examined with real-time RT-PCR (n=6). Molecular simulation was used to investigate the inhibition effect of XST on the CES1A protein. The CAMD pharmacodynamics and CES1A metabolism were investigated to evaluated the herb-drug interaction. Results. Clopidogrel and XST coadministration appreciably increased the Cmax, AUC, and MRT of CAMD. However, the expression of CES1A mRNA was decreased accordingly. It also indicated that the bioactive components in XST had good interaction with the CES1A metabolism target by molecular simulation. The animal study indicated that clopidogrel and XST coadministration produced significant herb-drug interactions at active CAMD pharmacokinetic and CES1A metabolic enzyme aspect. Conclusion. 30-days dose of coadministration altered hepatic CES1A protein and resulted in reduced plasma levels of active CAMD. both the decreased CES1A mRNA expression and the inhibition on the protein were due to the combination of XST, which accordingly upregulated the pharmacokinetics of plasma active CAMD