Adaptive Fireworks Algorithm

Junzhi Li, Shaoqiu Zheng, Ying Tan

Abstract: In this paper, firstly, the amplitude used in the Enhanced Fireworks Algorithm (EFWA) is analyzed and its lack of adaptability is revealed, and then the adaptive amplitude method is proposed where amplitude is calculated according to the already evaluated fitness of the individuals adaptively. Finally, the Adaptive Fireworks Algorithm (AFWA) is proposed, replacing the amplitude operator in EFWA with the new adaptive amplitude. Some theoretical analyses are made to prove the adaptive explosion amplitude a promising method. Experiments on CEC13's 28 benchmark functions are also conducted in order to illustrate the performance and it turns out that the AFWA where adaptive amplitude is adopted outperforms significantly the EFWA and meanwhile the time consumed is not longer. Moreover, according to experimental results, AFWA performs better than the Standard Particle Swarm Optimization (SPSO).