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Optimizing chemotherapy treatment outcomes using metaheuristic optimization algorithms: A case study.

Prakas Gopal Samy1,2, Jeevan Kanesan1, Irfan Anjum Badruddin3

  • 1Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

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This study models cancer and effector cell dynamics under chemotherapy using ordinary differential equations. Findings show specific algorithms perform best for different control problems, impacting treatment effectiveness.

Keywords:
Multi-objective optimal control problembifurcation analysismetaheuristic optimization algorithmsstability analysis

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Area of Science:

  • Mathematical Oncology
  • Computational Biology
  • Systems Biology

Background:

  • Explores a mathematical model using ordinary differential equations (ODEs) to simulate cancer and effector cell interactions during chemotherapy.
  • Analyzes model equilibrium point stability via Jacobian matrix and eigenvalues.
  • Conducts bifurcation analysis to identify optimal control parameter values.

Purpose of the Study:

  • To evaluate model and control strategy performance using benchmarking simulations.
  • To compare metaheuristic optimization algorithms for solving multi-objective optimal control problems.
  • To determine the efficacy of different algorithms in solving Pure and Hybrid Multi-objective Optimal Control Problems.

Main Methods:

  • Employs ordinary differential equations for modeling cancer-effector cell dynamics.
  • Utilizes Jacobian matrix and eigenvalues for stability analysis.
  • Applies metaheuristic optimization algorithms to solve Pure Multi-objective Optimal Control Problems (PMOCP) and Hybrid Multi-objective Optimal Control Problems (HMOCP).
  • Uses the Hypervolume (HV) indicator for algorithm performance comparison.

Main Results:

  • The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm demonstrated superior performance in solving the HMOCP.
  • The M-MOPSO algorithm showed better results for the PMOCP based on Hypervolume (HV) analysis.
  • Benchmarking simulations were conducted on the PlatEMO platform to validate model performance.

Conclusions:

  • Stability shifts at critical thresholds identified in the model may influence chemotherapy treatment efficacy.
  • While not directly clinical, the study provides insights into optimizing control parameters for potential therapeutic strategies.
  • The choice of metaheuristic algorithm is crucial for effectively solving different forms of multi-objective optimal control problems in cancer therapy modeling.