Exponentially Decaying Explosion in Fireworks Algorithm

Fireworks algorithm (FWA) as an efficient and robust swarm intelligence algorithm can successfully deal with complex multi-modal problems. In this paper, a novel new explosion operator called exponentially decaying explosion is proposed to enhance the local search ability of fireworks algorithm based on the principle of utilizing more information. The proposed method takes the idea of guided mutation a step further and dismantled the explosion process into an exponentially decaying series of guided explosion. The FWA variant with this explosion operator is called exponentially decaying fireworks algorithm (EDFWA). Theoretical analysis proved the superiority of EDFWA in terms of information utilization ratio compared with GFWA. Experimental results showed that EDFWA not only surpassed LoTFWA in low dimensional situations, but also exhibited powerful searching capability on 1000 dimensional high dimensional problems compared with multiple representative optimizers specially designed for large-scale problems.