Abstract：Aiming at the problems of slow convergence speed and low precision of Fireworks Algorithm,this paper proposes an improved algorithm of fireworks algorithm,which uses cauchy mutation with stronger global search ability to replace gaussian mutation,so as to increase the range of variation and improve the global search ability.The new explosion radius is constructed by using the location information of globally optimal fireworks individuals and historical cauchy sparks,so that it can not only inherit and learn historical information,but also adjust the step size adaptively.An “elite-random” selection strategy is proposed,which can give consideration to both the quality and distribution of fireworks.Simulation results of ten typical benchmark functions and ten 0-1 knapsack problems show that,compared with Bat Algorithm(BA),Particle Swarm Algorithm(SPSO),Particle Swarm Algorithm with Gaussian Mutation(GPSO),Fireworks Algorithm(FWA),Enhanced Fireworks Algorithm(EFWA),and Adaptive Fireworks Algorithm(AFWA).The algorithm has better convergence speed,accuracy and stability.
2020, Vol. 41 
