Glider Snake Optimization (GSO)

Overview

Glider Snake Optimization (GSO) is a novel nature-inspired metaheuristic optimization algorithm motivated by the gliding and serpentine locomotion behavior of arboreal snakes. The algorithm models the search process as a cooperative chain of agents, where each agent updates its position based on both global leader guidance and local predecessor interaction. This mechanism enables an effective balance between exploration and exploitation, particularly in complex and high-dimensional optimization problems.

This repository provides the official Python implementation of the GSO algorithm used in the experimental evaluations reported in the corresponding research paper.

Repository Contents

The repository includes the following files:

• GSO.py: Core Python implementation of the Glider Snake Optimization algorithm
• README.md: Documentation and usage instructions

Requirements

The implementation requires:

• Python 3.8 or later
• NumPy library
• random (standard library)

Install NumPy using:

pip install numpy

Usage

The GSO function optimizes continuous problems. Define your fitness function, then call GSO with parameters.

Example: Sphere Function

import numpy as np

def sphere(x):
    return np.sum(x**2)

result = GSO(
    fitness_func=sphere,
    sol_count=10,
    dimensions=30,
    iterations_count=100,
    lower_bound=-100,
    upper_bound=100,
    mutation_rate=0.5,
    verbose=True
)

print(f"Best fitness: {result.leader_fitness}")
print(f"Best solution: {result.leader_solution}")

Parameters

Parameter	Type	Default	Description
fitness_func	function	-	Objective function to minimize
sol_count	int	-	Population size (recommended: 10)
dimensions	int	-	Problem dimensionality
iterations_count	int	-	Max iterations (recommended: 100)
lower_bound	float/list	-	Lower search bound(s)
upper_bound	float/list	-	Upper search bound(s)
mutation_rate	float	0.5	Probability for chain mutation
stopping_func	function	None	Custom stopping criterion
plt_func	function	None	Plotting callback
verbose	bool	True	Print progress

Citation

Cite the GSO paper if used in research.