What is Particle swarm optimization?
Particle swarm optimization (PSO), which was first introduced by Kennedy and Eberhart in 1995, is a bio-inspired algorithm that mimics the social behaviour of a flock of birds or a school of fish. It is used to search for an optimal solution from a set of possible soloutions.
This algorithm has been used to solve a wide range of optimization problems in many fields, such as engineering, economics, and machine learning. It is effective in solving problems that are difficult or impossible to solve with simple traditional techniques.
How Particle Swarm Optimization works?
In summary, the algorithm operates by creating a swarm of particles, in which each represnt a possible soloution to an optimzation problem
These particles move around a search space, adjusting their position according to their own data (personal best) and the knowlege feeback from neighboring particles.
Every particle would have their own position vector, which determines the current soloution candidate, and velocity vector, that represents the direction and magnitude of the particles movement.
Each particles position and velocity are updated based on its own optimum solution and the best solution of the swarm.
The algorithm starts by initializing a swarm of particles with random positions and velocities in the search space. Then, the particles move in the search space iteratively.
At each iteration, the position and velocity of each particle are updated based on the following equations:
- Velocity update:
vi(t+1) = wvi(t) + c1r1(pi - xi(t)) + c2r2(pg - xi(t)) - Position update:
xi(t+1) = xi(t) + vi(t+1)
where vi(t) and xi(t) are the velocity and position of particle i at iteration t, respectively; w, c1, and c2 are the inertia weight, cognitive parameter, and social parameter, respectively; r1 and r2 are random numbers between 0 and 1; pi is the best position of particle i so far; and pg is the best position of the swarm so far.
(Xu et al.,2016)
applications of particle swarm optimization
– Neural networks: PSO can be used to train neural networks by optimizing the weights and improving their accuracy.
– Bioinformatics: this algorithim can also optimize the parameters of many bioinformatics algorithms, such as protein folding, DNA sequencing.
Portfolio: They can also be utlized in optimzing the portfolios of assets, and this can reduce risks.
references
Xu, X., Rong, H., Trovati, M., Liptrott, M., & Nik, N. (2016). CS-PSO: Chaotic Particle Swarm Optimization Algorithm for Solving Combinatorial Optimization Problems. Journal of Computers, 11(7), 710-722.