Overview / Usage

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented.In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the adaptive parameters, to avoid it falling into local extreme of population. The experimental results show the improved algorithm to improve the optimization ability of the algorithm.

Methodology / Approach

The algorithm is an improvement upon the famous Particle Swarm Optimization algorithm. The implementation can be broadly broken down into three steps.

1. Initialize Control Parameters
2. Initialize population of particles
3. Iteratively compute the local and global best till end condition is satisfied.

To run the algorithm:

• Clone the repository
• Change the cost function from Sphere.m to the required function
• Open up MATLAB command prompt and run qpso1.m

Technologies Used

MATLAB 2018b

Repository

https://github.com/ritwikraha/Particle-Swarm-Optimization-using-Matlab