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.
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.
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.
/* Starting phase */
Parameters initializing: Fitness evaluation π, Max fitness evaluation πππ₯πΉ, Population size π,
Dimension π·.
Randomly initialize the agent population π΄π,π· and evaluate their fitness πππ‘π
,
Find the current optimal π΄πππ π‘.
π = π + π.
/* Main loop*/
While π < πππ₯πΉ
Calculate the probability πΎ, exponent π.
For each agent π = 1 βΆ π
For each dimension π = 1 βΆ π·
/* Comprehensive elimination phase */
If rand<πΎ
Update search agent ππ,π using Eq. (7).
End If
/* Local clearance phase */
Update search agent ππ,π using Eq. (8)
/* Post-consolidation phase */
Search agent information crossover by Eq. (11)
End For
End For
Calculate the fitness πππ‘.
Update the population and find the optimal.
π = π + π
End While
Return the optimal solution
# 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.
- 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.
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.
To compare the performance of the Artemisinin Optimization algorithm with other optimization algorithms provided in this repository, follow these steps:
-
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
-
Extract the contents of each zip file to your local machine.
-
Run each optimization algorithm using the provided code or executable files.
-
Evaluate and compare the performance of each algorithm based on predefined metrics or criteria.
-
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.
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: [email protected]
- Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China
-
Dong Zhao
- Email: [email protected]
- Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China
-
Ali Asghar Heidari
- Email: [email protected], [email protected]
- Affiliation: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
-
Huiling Chen
- Email: [email protected]
- Affiliation: Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
We extend our gratitude to all contributors and organizations that supported the development of the Artemisinin Optimization algorithm.
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.
If you have any questions, feedback, or collaboration inquiries, feel free to reach out to us:
-
Chong Yuan
- Email: [email protected]
- Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China
-
Dong Zhao
- Email: [email protected]
- Affiliation: College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China
-
Ali Asghar Heidari
- Email: [email protected], [email protected]
- Affiliation: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
-
Huiling Chen
- Email: [email protected]
- Affiliation: Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
If you use the Artemisinin Optimization algorithm in your academic research, please cite our paper
For additional resources, supplementary files, and open-source code, visit aliasgharheidari.com/AO.html.
Find the Artemisinin Optimization algorithm on MathWorks File Exchange: Artemisinin Optimization - Algorithm and Application to Medical Image Segmentation
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!