aliasgharheidaricom / Artemisinin-Optimizer-using-Malaria-Therapy-Algorithm-and-Applications-to-Medical-Image-Segmentation

The source codes of Artemisinin Optimization are also publicly available at https://aliasgharheidari.com/AO.html, This study presents the analysis and principle of AO algorithm to optimize different problems.

Artemisinin Optimization based on Malaria Therapy: Algorithm and Applications to Medical Image Segmentation

Version 1.0, uploaded on 5 11 2024, has been developed based on the Artemisinin Optimization (AO) algorithm introduced in the paper "Artemisinin Optimization based on Malaria Therapy: Algorithm and Applications to Medical Image Segmentation" authored by Chong Yuan, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, Zongda Wu, and Huiling Chen.

Introduction

Welcome to the Artemisinin Optimization repository! The Artemisinin Optimization (AO) algorithm is an efficient metaheuristic algorithm inspired by the process of artemisinin medicine therapy for malaria. This repository contains the source code for the AO algorithm, along with comprehensive documentation to aid in understanding and utilizing this powerful optimization tool.

Overview

This repository houses the implementation of the AO algorithm, which consists of three optimization stages: comprehensive eliminations, local clearance, and post-consolidation. The algorithm is designed to simulate the process of artemisinin therapy for malaria, with a focus on global exploration, local exploitation, and the ability to escape local optima.

Algorithm Pseudo-code

/* Starting phase */
Parameters initializing: Fitness evaluation 𝑓, Max fitness evaluation 𝑀𝑎𝑥𝐹, Population size 𝑁,
Dimension 𝐷.
Randomly initialize the agent population 𝐴𝑁,𝐷 and evaluate their fitness 𝑓𝑖𝑡𝑖
,
Find the current optimal 𝐴𝑏𝑒𝑠𝑡.
𝑓 = 𝑓 + 𝑁.
/* Main loop*/
While 𝑓 < 𝑀𝑎𝑥𝐹
Calculate the probability 𝐾, exponent 𝑐.
For each agent 𝑖 = 1 ∶ 𝑁
 For each dimension 𝑗 = 1 ∶ 𝐷
 /* Comprehensive elimination phase */
 If rand<𝐾
 Update search agent 𝑎𝑖,𝑗 using Eq. (7).
 End If
 /* Local clearance phase */
 Update search agent 𝑎𝑖,𝑗 using Eq. (8)
 /* Post-consolidation phase */
 Search agent information crossover by Eq. (11)
 End For
End For
Calculate the fitness 𝑓𝑖𝑡.
Update the population and find the optimal.
𝑓 = 𝑓 + 𝑁
End While
Return the optimal solution

Usage Example:

# Example usage of the AO algorithm
from AO import ArtemisininOptimization

# Set parameters
N = 100  # Number of individuals in the population
Max_iter = 1000  # Maximum number of iterations
lb = -5  # Lower bound for optimization variables
ub = 5  # Upper bound for optimization variables
dim = 10  # Dimensionality of the problem

# Define objective function to be minimized
def fobj(x):
    # Example objective function
    return sum(x**2)

# Run AO algorithm
optimizer = ArtemisininOptimization(N, Max_iter, lb, ub, dim, fobj)
best_solution = optimizer.optimize()

print("Best solution:", best_solution)

Note: Replace the objective function fobj with your specific objective function.

Key Features

• Efficient metaheuristic algorithm inspired by artemisinin therapy for malaria.
• Three optimization stages: comprehensive eliminations, local clearance, and post-consolidation.
• Designed for global exploration and the ability to escape local optima.

Applications

Explore the applications of the Artemisinin Optimization algorithm in medical image segmentation:

• Multi-Threshold Image Segmentation (MTIS)
  • Learn about our approach combining AO algorithm for enhanced segmentation of medical images.

For detailed experiments, evaluation metrics, and results, refer to the Applications.md file.

Comparing Artemisinin Optimization with State-of-the-art Algorithms

To compare the performance of the Artemisinin Optimization algorithm with other optimization algorithms provided in this repository, follow these steps:

1. Download the zip files containing the other optimization algorithms from the repository:

   • Compare Artemisinin Optimization with Harris Hawk Optimizer (HHO).zip
   • Compare Artemisinin Optimization with Hunger games search (HGS).zip
   • Compare Artemisinin Optimization with Parrot Optimizer (PO).zip
   • Compare Artemisinin Optimization with RIME optimizer.zip
   • Compare Artemisinin Optimization with Runge Kutta Optimization (RUN).zip
   • Compare Artemisinin Optimization with Slime mould algorithm (SMA).zip
   • Compare Artemisinin Optimization with Weighted Mean of Vectors (INFO) optimizer.zip

2. Extract the contents of each zip file to your local machine.

3. Run each optimization algorithm using the provided code or executable files.

4. Evaluate and compare the performance of each algorithm based on predefined metrics or criteria.

5. Share your findings and insights with the community by contributing to the respective repositories or sharing your analysis in academic papers or forums.

Feel free to explore and analyze the performance of the Artemisinin Optimization algorithm compared to other state-of-the-art optimization algorithms. If you have any questions or need assistance, don't hesitate to reach out to the authors or the respective communities of each optimization algorithm.

Contributing

We welcome contributions from the community! If you'd like to contribute to the Artemisinin Optimization project, please contact us. If you have any questions, suggestions, or feedback, feel free to reach out to the authors:

• Chong Yuan

  • Email: [e-mail]
  • Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China

• Dong Zhao

  • Email: [e-mail]
  • Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China

• Ali Asghar Heidari

  • Email: [e-mail], [e-mail]
  • Affiliation: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

• Huiling Chen

  • Email: [e-mail]
  • Affiliation: Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China

Acknowledgments

We extend our gratitude to all contributors and organizations that supported the development of the Artemisinin Optimization algorithm.

License

The Artemisinin Optimization algorithm is licensed under the MIT License. Please review the license for details on how you can use and distribute this software.

Contact Information

If you have any questions, feedback, or collaboration inquiries, feel free to reach out to us:

• Chong Yuan

  • Email: [e-mail]
  • Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China

• Dong Zhao

  • Email: [e-mail]
  • Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China

• Ali Asghar Heidari

  • Email: [e-mail], [e-mail]
  • Affiliation: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

• Huiling Chen

  • Email: [e-mail]
  • Affiliation: Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China

Citation

If you use the Artemisinin Optimization algorithm in your academic research, please cite our paper

Supplementary Files and Code

For additional resources, supplementary files, and open-source code, visit aliasgharheidari.com/AO.html.

MATLAB File Exchange

Find the Artemisinin Optimization algorithm on MathWorks File Exchange: Artemisinin Optimization - Algorithm and Application to Medical Image Segmentation

Flowchart

Feel free to explore the code, contribute to the project, and leverage the Artemisinin Optimization algorithm for your optimization needs. Thank you for your interest!