Switched system optimal control approach for drug administration in cancer chemotherapy

Wu, Xinru; Hou, Yuzhou; Zhang, Kanjian

doi:10.1016/j.bspc.2022.103575

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Similarly, the ODE has been shown in equations ( 1) to (3) solved with approximate intervals and boundaries for control input. The numerical solution of this MOOCP was later incorporated with the penalty method so that the solution of MOOCP can converge to constraints [47].…”

Section: Methodsmentioning

confidence: 99%

Optimization of Chemotherapy Using Hybrid Optimal Control and Swarm Intelligence

2023

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This study aimed to minimize the tumor cell population by using minimal medicine for chemotherapy treatment while maintaining the effector-immune cell population at a healthy threshold. Therefore, a mathematical model was developed in the form of ordinary differential equations (ODE), and the solution to Multi-Objective Optimal Control Problem (MOOCP) was obtained using Multi-Objective Optimization algorithms. In this study, the interaction of the tumor-effector cell population with chemotherapy was investigated using Pure MOOCP and Hybrid MOOCP methods. The handling of constraints and the Pontryagin Maximum Principle (PMP) differ among these methods. Swarm Intelligence (SI) and Evolutionary Algorithms (EA) were used to process the results of these methods. The numerical outcomes of SI and EA are displayed via Pareto Optimal Front. In addition, the solutions from these algorithms were further analyzed using the Hypervolume Indicator. The findings of this study demonstrate that the hybrid method outperforms Pure MOOCP via Multi-Objective Differential Evolution (MODE). The MODE produces a point on the Pareto Optimal Front with minimal distance to the origin, where the distance represents the best solution.

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Section: Methodsmentioning

confidence: 99%

Optimization of Chemotherapy Using Hybrid Optimal Control and Swarm Intelligence

2023

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“…This may result in the ultimate demise of the human due to these anomalies. Thus, effective treatment options for cancer are a widely explored area in literature and practice for their potential to save countless human lives [2]- [6]. Although the size of a malignant mass is frequently described with reference to the number of cells, nonetheless, cancer cells generally multiply exponentially.…”

Section: Introductionmentioning

confidence: 99%

Robust Control for Cancer Chemotherapy Through Time Delayed Estimation Philosophy

Sarkar,

Saidani,

Almuqren

et al. 2024

IEEE Access

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Chemotherapy, a vital cancer treatment, operates by delivering drugs to target and eliminate cancer cells in the patient body. Mathematical models like the log-kill, Norton-Simon, and E max hypotheses describe the growth/shrinking of the cancerous tumor due to the interaction and administration of the drugs with the tumor. This paper proposes a robust control approach based on artificial time-delayed theory to track the desired rate of change in tumor volume under model uncertainties and disturbances. The proposed algorithm relaxes the assumption on a priori knowledge of disturbance bound and its derivative. Unlike traditional methods, the control structure is simple, and the total disturbance is estimated by analyzing the previous input and output of the feedback state and control variables. Thus, robustness is ensured without relying on high-frequency switching or high gain. The stability analysis of the proposed scheme is investigated based on the Lyapunov theory. Moreover, extensive simulation results with comparative analysis affirm the efficacy of the proposed approach.

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“…Generally speaking, we can divide these algorithms into two categories: deterministic algorithm (DA) and probabilistic algorithm (PA). The DA involves the trajectory approach, 19 auxiliary function approach, 20 covering approach, 21 etc. Simulated annealing algorithm, 22 genetic algorithm, 23 differential evolution, 24 and particle swarm optimization 25 are several typical algorithms of the PA.…”

Section: Introductionmentioning

confidence: 99%

Numerical algorithm for hypersonic vehicle optimal flight control

Zhang

Ding

2023

Measurement and Control

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We consider a hypersonic vehicle optimal flight control problem. The problem is modeled as an optimal control problem of switched systems (OCPSS), which can become a parameter optimization problem (POP). Following that, to achieve the globally optimal solution of the POP, an improved continuous filled function (CFF) algorithm including one adjusting parameter is proposed based on a penalty function, in which the CFF is differentiable, excludes logarithmic terms or exponential terms, and does not require to minimize the cost function. Numerical results show that the proposed algorithm is effective.

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Switched system optimal control approach for drug administration in cancer chemotherapy

Cited by 12 publications

References 43 publications

Optimization of Chemotherapy Using Hybrid Optimal Control and Swarm Intelligence

Optimization of Chemotherapy Using Hybrid Optimal Control and Swarm Intelligence

Robust Control for Cancer Chemotherapy Through Time Delayed Estimation Philosophy

Numerical algorithm for hypersonic vehicle optimal flight control

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