Electrical Storm Optimization (ESO)

A novel metaheuristic optimization algorithm inspired by electrical storm dynamics in nature. ESO combines swarm intelligence with adaptive field mechanics to efficiently explore complex, multi-dimensional search spaces.

Key Features

• Dynamic Field Adaptation: Automatically adjusts field intensity and resistance based on the search landscape
• Intelligent Branching: Lightning propagation mechanisms that balance exploration and exploitation
• Ionized Areas: Strategic use of promising regions to guide the search process
• Memory Optimization: Built-in caching system to avoid redundant function evaluations
• Robust Error Handling: Comprehensive management of numerical edge cases
• Performance Tracking: Detailed history of metrics and optimization progress

Quick Start

from ESO import ESO

# Define your objective function
def sphere(x):
    return sum(xi**2 for xi in x)

# Configure optimizer
optimizer = ESO(
    function=sphere,          # Objective function to minimize/maximize
    pop_size=50,             # Population size (number of lightning bolts)
    max_iter=1000,           # Maximum iterations
    max_eval=500000,         # Maximum function evaluations
    objective='min',         # 'min' for minimization, 'max' for maximization
    bounds=[(-10, 10)]*2,    # Search space bounds per dimension
    verbose=True             # Enable progress output
)

# Run optimization
best_position, best_score = optimizer.optimize()

Advanced Usage

# Example with custom settings and constraints
optimizer = ESO(
    function=your_function,
    pop_size=100,            # Larger population for complex problems
    max_iter=2000,           # Extended iteration limit
    max_eval=1000000,        # Increased evaluation budget
    objective='max',         # Maximization problem
    bounds=[(0, 1)]*5,       # 5-dimensional problem with [0,1] bounds
    verbose=True
)

# Access optimization history
print(f"Field Intensity History: {optimizer.field_intensity_history}")
print(f"Field Resistance History: {optimizer.field_resistance_history}")
print(f"Storm Power History: {optimizer.storm_power_history}")

Algorithm Parameters

• function: Target objective function to optimize
• pop_size: Number of lightning bolts in the population
• max_iter: Maximum number of iterations
• max_eval: Maximum number of function evaluations
• objective: Optimization direction ('min' or 'max')
• bounds: List of tuples defining the search space boundaries
• verbose: Enable/disable progress output

Performance Monitoring

The algorithm tracks various metrics during optimization:

• Function evaluation history
• Field intensity and resistance evolution
• Storm power dynamics
• Best solution progression
• Iteration timing statistics

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use ESO in your research, please cite:

@article{eso2025,
    title={Electrical Storm Optimization (ESO) Algorithm: Theoretical Foundations, Analysis, and Application to Engineering Problems},
    author={[Soto Calvo M.; Lee Han S.]},
    journal={[Mach. Learn. Knowl. Extr.]},
    doi={[[DOI](https://doi.org/10.3390/make7010024)]},
    year={2025}
}

Disclaimers

• The Matlab implementation of the ESO is still under development and its performance is not yet ensured.