A novel hybrid dynamic fireworks algorithm with particle swarm optimization

In recent years, the fireworks algorithm (FWA) has attracted more and more attention due to its strong ability to solve optimization problems. However, the global performance of FWA is significantly affected by the explosion amplitude. In this paper, a dynamic fireworks algorithm with particle swarm optimization (DFWPSO) is developed to improve the global performance of FWA. In DFWPSO, a dynamic explosion amplitude mechanism based on the evolution speed of population, which is dynamically adjusted by evaluating the evolution speed of fitness in each iteration process, is designed to control the global and local searching information. Moreover, a new nonlinear minimal amplitude check strategy based on function decreasing is designed to obtain appropriate amplitude. Furthermore, a new firework updating mechanism based on particle swarm optimization (PSO) is implemented to accelerate the convergence of algorithm and cut down on computing resources. In addition, the selection operator of FWA is abandoned and all fireworks are updated by velocity and current location in each iteration process. To verify the performance of the proposed DFWPSO algorithm, three groups of the benchmark functions are used and tested for experiments. Compared with other variants of FWA and PSO variants, results show that the proposed algorithm performs competitively and effectively.

Automated summary

A dynamic fireworks algorithm with particle swarm optimization (DFWPSO) is proposed in this paper to enhance the global performance of FWA by dynamically adjusting explosion amplitude and implementing a new update mechanism; experimental results demonstrate the competitive and effective performance of the algorithm in solving optimization problems.