This repository contains implementations of various optimization algorithms for different problems. Each subdirectory contains code and examples for a specific algorithm applied to a specific problem.
- Genetic Algorithm for Travelling Salesman Problem
- Genetic Algorithm for Sine Wave
- Cultural Algorithm for Sine Wave
- Particle Swarm Optimization for Sine Wave
- Ant Colony Optimization for Shortest Path
This section contains the implementation of a Genetic Algorithm to solve the Travelling Salesman Problem.
python tsp_genetic_algorithm.pyThe Travelling Salesman Problem involves finding the shortest possible route that visits a given set of cities and returns to the original city. The Genetic Algorithm is applied here to find an approximate solution.
This section contains the implementation of a Genetic Algorithm to approximate a sine wave.
python sine_wave_genetic_algorithm.pyThe Genetic Algorithm is used to evolve a set of parameters that approximate a sine wave.
This section contains the implementation of a Cultural Algorithm to approximate a sine wave.
python sine_wave_cultural_algorithm.pyThe Cultural Algorithm is a population-based optimization technique inspired by the concept of cultural evolution. It is applied here to approximate a sine wave.
This section contains the implementation of a Particle Swarm Optimization algorithm to find both maxima and minima of a sine wave.
python sine_wave_pso.pyParticle Swarm Optimization is a population-based stochastic optimization technique inspired by the social behavior of birds and fish. It is applied here to find both maxima and minima of a sine wave.
This section contains the implementation of an Ant Colony Optimization algorithm to find the shortest path in a graph.
python ant_colony_optimization.pyAnt Colony Optimization is a nature-inspired algorithm based on the behavior of ants searching for the shortest path between their nest and a food source. It is applied here to find the shortest path in a graph.