A novel Bat Algorithm based on chaos for optimization tasks

Difficulties of tackling real-world problems with their growing complexities motivated computer scientists to search for more efficient problem solving approaches. Metaheuristic algorithms are outstanding examples of these ap-proaches. Bat Algorithm (BA) is a new meta-heuristic optimization algorithm, which has been developed rapidly and has been applied in different optimization tasks in recent years. In this paper an improved version of Bat algorithm with chaos is represented. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences for parameter initialization. Simulation results on some mathematical benchmark functions demonstrate the validity of proposed algorithm, in which the Chaotic Bat Algorithm (CBA) outperforms the classical BA.