Particle-Swarm-Optimization-using-Matlab

Introduction

Swarm Intelligence is a branch of Artificial Intelligence where we observe nature and try to learn how different biological phenomena can be imitated in a computer system to optimize the scheduling algorithms. In swarm intelligence, we focus on the collective behavior of simple organisms such as ants,fishes or birds and their interaction with the environment.

Proposed in 1995 by J. Kennedy an R.Eberhart, the article Particle Swarm Optimization became very popular due to this continue optimization process allowing variations to multi targets and more. Consisting in the constant search of best solution, the method moves the particles with a certain velocity calculated in every iteration. Each particles movement has the influence of his own the best known position and also the best known position in the space-search, known as the global best position. The final result expected is that the particle swarm converge to the best solution. Its important to mention that PSO doesnt use Gradient Descent, so it can be used to non linear problems once it doesnt require that the problem have to be differentiable.

In this repository we will be trying to implement the basic PSO algorithm as given below using Matlab from scratch. The repository includes two sub-folders namely 'pso-basic' and 'pso-func'. Any beginner is recommended to start at 'pso-basic'. To understand the inspiration behind the algorithm and the mathematics governing the model have a look at the original paper.

Algorithm

Initialize the controlling parameters (N,c1,c2,Wmin,Wmax,Vmax,MaxIter)
Initialize the population of N particles
do
	for each particle
		calculate the objective of the particle
		update PBEST if required
		update GBEST if required
	end for
	update the inertia weight
	for each particle
		update velocity (v)
		update position (x)
	end for
while the end condition is not satisfied
return GBEST as the best estimation of global optimum