Backward Fireworks Algorithm and Application Research

Aiming at the performance bottlenecks and slow convergence of fireworks algorithm(FWA),an adaptive backward learning operator(ABLO)is proposed through the introduction of backward learning strategy,and its convergence performance is proved theoretically.By combining FWA with ABLO,a set of hybrid FWA is proposed and verified by typical test functions.The results show that under the same experimental setup,the backward fireworks algorithm can improve the computation accuracy by at least 10-2 in the optimization performance of original algorithm and the convergence rate is enhanced.Finally the algorithm is applied to identify the parameters of Lorenz chaotic system.Through simulation experiments,it is verified that this algorithm can be used for parameter identification of chaotic control systems.Compared with other swarm intelligence algorithms,its identification error is as low as 10-11.It is a novel and effective parameter identification method for chaotic control systems.