The performance analysis of swallow swarm optimization algorithm

Soft Computing is a term used in computer science to refer to problems in computer science whose solutions are unpredictable, uncertain and between 0 and 1. Optimization is the selection of a best element from some set of available alternatives which is carried out in research, industry and engineering. The existing optimization algorithms in soft computing are Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), Particle Swarm Optimization (PSO), and Artificial Fish Swarm Optimization (AFSO). In ACO, the Pheromone evaporation has the advantage of avoiding the convergence to a locally optimal solution. Bee Colony Optimization (BCO), where the scope of the local exploration is progressively focused on the area immediately close to the local best fitness. Particle Swarm Optimization (PSO) is a pattern search method which does not use the gradient of the problem being optimized. Artificial Fish Swarm Optimization (AFSO), which has faster convergence speed and require few parameters to be adjusted. Swallow Swarm Optimization (SSO) has high efficiency and high convergence speed and not getting stuck in local extreme points. In this paper, the experiments have been carried out which deals with the comparison of PSO, FSO and SSO algorithms with different parameters. The swallow swarm optimization algorithm has been proven to have faster convergence speed of getting the optimal result at lower number of iterations. We also present the design and performance evaluation of SSO.