Polar Lights Optimizer (PLO): Algorithm and Applications in Image Segmentation and Feature Selection
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Polar Lights Optimizer in action
Polar Lights Optimization (PLO) is a metaheuristic algorithm inspired by the mesmerizing aurora phenomenon. By simulating high-energy particles influenced by Earth's magnetic field, PLO achieves a balance between local exploitation and global exploration. This method is highly effective in complex optimization tasks such as image segmentation and feature selection.
This study introduces Polar Lights Optimization (PLO), an algorithm based on the aurora phenomenon or polar lights. The aurora is a unique natural spectacle that occurs when energetic particles from the solar wind converge at the Earth's poles, influenced by the geomagnetic field and the Earth's atmosphere. By analyzing the motion of high-energy particles and delving into the underlying principles of physics, we propose a unique model for mimicking particle motion. This model integrates gyration motion and aurora oval walk, with the former facilitating local exploitation, while the latter enabling global exploration. By synergistically combining these two strategies, the proposed PLO achieves a balanced approach to local exploitation and global exploration. Additionally, a particle collision strategy is introduced to enhance the efficiency of escaping local optima. To evaluate the performance of PLO, a qualitative analysis experiment is designed to assess its ability to explore the problem space and search for solutions. PLO is compared against 9 classic algorithms and 8 high-performance algorithms using 30 benchmark functions from IEEE CEC2014. Furthermore, we compare and analyze PLO with the current state-of-the-art methods in the field, utilizing 12 benchmark functions from IEEE CEC2022. Subsequently, PLO is successfully applied to multi-threshold image segmentation and feature selection. Specifically, a PLO-based multi-threshold segmentation model and a binary PLO-based feature selection method are developed. The performance of PLO is also evaluated using 10 images from the Invasive Ductal Carcinoma (IDC) medical dataset, while the overall adaptability and accuracy of the feature selection model are tested using 8 medical datasets. These results affirm the emergence of PLO as an effective optimization tool ready for solving real-world problems, including those in the medical field.
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Prerequisites:
- MATLAB (R2017b or later recommended)
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Clone the repository:
git clone https://github.com/yourusername/PLO.git cd PLO
To run the PLO algorithm, use the MATLAB function:
[Best_pos, Bestscore, Convergence_curve] = PLO(N, MaxFEs, lb, ub, dim, fobj)N: Number of particles in the swarmMaxFEs: Maximum number of function evaluationslb: Lower bound of the solution spaceub: Upper bound of the solution spacedim: Dimension of the solution spacefobj: Objective function handle
N = 30; % Number of particles
MaxFEs = 10000; % Maximum function evaluations
lb = -10; % Lower bound
ub = 10; % Upper bound
dim = 2; % Dimension of the problem
fobj = @(x) sum(x.^2); % Objective function (e.g., sphere function)
[Best_pos, Bestscore, Convergence_curve] = PLO(N, MaxFEs, lb, ub, dim, fobj);Inspired by the aurora phenomenon, PLO models the interaction of solar wind particles with Earth's magnetic field. The algorithm utilizes:
- Gyration Motion: Simulates spiraling motion around magnetic field lines.
- Aurora Oval Walk: Mimics the formation of auroral ovals for global exploration.
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Initialization:
- Create an initial population of particles within the bounds.
- Evaluate fitness.
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Main Loop:
- Update velocities and positions using gyration and aurora oval strategies.
- Apply particle collision for escaping local optima.
- Update fitness and track the best solution.
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Gyration Motion:
- Models centripetal force around magnetic field lines.
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Aurora Oval Walk:
- Simulates elliptical movement with Levy flight.
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Particle Collision:
- Facilitates escape from local optima through random collisions.
Initialize population X with random positions within bounds [lb, ub]
Evaluate fitness of each particle in X
Sort particles based on fitness
Set Bestpos and Bestscore to the best particle and its fitness
While number of function evaluations (FEs) < MaxFEs
Calculate global mean position X_mean
Calculate weights w1 and w2 for gyration and aurora oval strategies
For each particle i in the population
Update velocity V[i] using gyration and aurora oval strategies
Update position X_new[i] based on V[i]
If particle collides (based on probability)
Apply collision strategy to X_new[i]
Ensure X_new[i] is within bounds [lb, ub]
Evaluate fitness of X_new[i]
If fitness of X_new[i] < fitness of X[i]
Update position X[i] to X_new[i]
Update fitness of X[i]
Sort particles based on fitness
Update Bestpos and Bestscore if a better solution is found
Increment FEs
Return Bestpos and Bestscore
This project is licensed under the MIT License.
- PLO Source Code and Documentation: Ali Asghar Heidari
- Main Paper: Chong Yuan, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, Huiling Chen. "Polar Lights Optimizer: Algorithm and Applications in Image Segmentation and Feature Selection," Neurocomputing - 2024.
For support or inquiries, reach out to:
- Chong Yuan: [email protected]
- Ali Asghar Heidari: [email protected]
- Huiling Chen: [email protected]
If you use this code, please cite:
- Chong Yuan, Dong Zhao, Ali Asghar Heidari, Lei Liu, Yi Chen, Huiling Chen. "Polar Lights Optimizer: Algorithm and Applications in Image Segmentation and Feature Selection," Neurocomputing - 2024.
Delve into how the Polar Lights Optimizer (PLO) stacks up against other cutting-edge optimization techniques:
- 🌟 PLO 2024
- 🚀 FATA 2024
- 🌐 ECO 2024
- 🔍 AO 2024
- ✨ PO 2024
- 🔬 RIME 2023
- 📊 INFO 2022
- 🛠️ RUN 2021
- 🔧 HGS 2021
- 🧩 SMA 2020
- 🌠 HHO 2019
Explore these methods to see how PLO compares and stands out in the field of optimization!