Particle Swarm Optimization

Scientists have always used nature as a source of inspiration. Particle Swarm Optimization (PSO) is another attempt of mathematicians and scientists to use nature to solve difficult problems. A concept of optimization of non-linear function using Particle swarm optimization is proposed. This paper will attempt to relay to the reader an understanding of PSOs and their use as a method for optimization of continuous non-linear functions. Particle Swarm Optimization has found itself useful for two major reasons: i) the development of PSO code is relatively simple and only takes a few lines of computer code and ii) PSOs find themselves computationally inexpensive as long as the function being solved for is computationally inexpensive. This work will also compare PSO techniques directly with genetic algorithms, cite examples of PSO uses and discuss the performance on Rosenbrock’s Banana Function and other classic multidimensional minimization examples