This repository contains a Python implementation of the Particle Swarm Optimization (PSO) algorithm and Firefly algorithm (FA). PSO is a computational method used to optimize a wide range of problems by iteratively improving a candidate solution with respect to a given measure of quality. The FA is inspired by the bioluminescent behavior of fireflies and is used to solve complex optimization problems by simulating the attraction and movement of fireflies towards brighter and more attractive solutions.
Particle Swarm Optimization (PSO) is a bio-inspired optimization algorithm developed by James Kennedy and Russell Eberhart in 1995. It is inspired by the social behavior of birds flocking or fish schooling. PSO is used to find the optimal solution by having a population (swarm) of candidate solutions (particles) move around in the search space according to simple mathematical formulas over the particle's position and velocity.
The Firefly Algorithm (FA) was developed by Xin-She Yang in 2008 and is based on the flashing behavior of fireflies. In the algorithm, each firefly represents a potential solution, and the brightness of a firefly is determined by the value of the objective function. Fireflies are attracted to brighter ones, and they move towards them, allowing the swarm to explore the search space and converge towards optimal solutions.