The core fireworks dynamic explosion radius strategy is used in dynamic search fireworks algorithm (dynFWA), it is an important algorithm to solve the optimization problem. DynFWA accuracy is low and is too early to fall into local optimal solutions. In order to improve the defects, the traditional dynamic search fireworks algorithm is improved by embedding two different learning factors, it is called as the improved dynFWA (IdynFWA). The learning factor of the algorithm makes full use of the individual fireworks information of each generation in the search process, the fireworks can search for information to the group’s excellent search information, its two different generating ways are helpful to balance the local search and global search ability. The improved algorithm is tested on 28 Benchmark functions of CEC2013. The experimental results show that IdynFWA is superior to dynFWA, it is better than particle swarm algorithm SPSO2011 and differential evolution algorithm DE optimize performance.
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