
We propose in this paper the use fuzzy logic to adjust parameters in the fireworks algorithm (FWA), that is, parameters that usually are considered as constants in the algorithm, we have transformed them to be dynamic parameters in the FWA. First, we realized an exhaustive experimentation of the parameters of the FWA algorithm, with the purpose of selecting the parameters that have more effect on the FWA performance, and we concluded that the main parameters of this algorithm are: numbers of sparks and the explosion amplitude of each firework. The modifications made to these parameters help us provide a better exploration and exploitation abilities to the algorithm. The main goal of this paper is to optimize the performance of the FWA. In this paper, we show the results of the modified algorithm, which we called fuzzy fireworks algorithm and we denoted as FFWA. The results of the experiments were obtained with 6 benchmarks functions.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
