Dynamic Search Hybrid Fireworks Algorithm

In order to enhance the local exploitation and global search capability of the fireworks algorithm, this paper proposes a dynamic search hybrid fireworks algorithm (DHFWA). Firstly, the algorithm utilizes the adaptive changes in fireworks to update the explosion amplitude of the fireworks. When the adaptive value improves, the explosion amplitude is increased to enhance the algorithm’s global search ability. Conversely, reducing the explosion amplitude improves the algorithm’s local utilization ability; then, replace the Gaussian mutation operator with a bootstrap mutation operator to enhance the information exchange between sparks generated by the same fireworks; finally, using differential evolution instead of the original selection strategy enhances the information exchange between fireworks and improves the convergence performance of the algorithm. Tests were conducted on the CEC2017 benchmark suite, and the experimental results show that DHFWA significantly outperforms previous fireworks algorithms.