Improve enhanced fireworks algorithm with differential mutation

Chao Yu, Junzhi Li, Ying Tan

Abstract: Fireworks algorithm (FWA) is a newly proposed swarm intelligence algorithm, which is used to solve optimization problems. However, the interaction of fireworks in FWA is not sufficient. In this paper, the differential mutation operator is introduced to improve the interaction mechanism of enhanced FWA (EFWA), which is the latest version of FWA. Extensive experiments on 30 benchmark functions were conducted to test the performance of the new algorithm named enhanced fireworks algorithm with differential mutation (FWA-DM). Experimental results have shown that differential mutation operator is able to improve EFWA.