Optimization of benchmark functions using a nature inspired bird swarm algorithm

This paper presents a new powerful Bird Swarm Algorithm (BSA) for optimization. BSA basically works on the swarm intelligence and interactions among the birds. The concept behind this algorithm is the exploitation and exploration of optimum solution for a given problem based on foraging, vigilance and flight behavior. Formulation of BSA includes four search strategies associated with five simplified rules. Mathematically models the behavior of bird swarm is utilized for solution of various mathematical functions. To validate the effectiveness of BSA simulations have been performed on various numerical functions and ELD problems. The results obtained by BSA have been also compared with other Nature-Inspired algorithms. The performance of BSA on the convergence rate to obtain the optimal result on changing the parameter is also observed. Statistical comparison of results affirms the superiority of BSA over other algorithms reported in recent literatures.