An improved firefly algorithm based on probabilistic attraction
Gan Yu · International Journal of Computing Science and Mathematics · 2016
Firefly algorithm (FA) is an efficient optimisation tool, which has been widely applied to various optimisation problems. However, the standard FA still has some drawbacks. For example, the computational time complexity of FA is higher than other swarm intelligence algorithms. Recently, Wang et al. (2016b) designed a random attraction model to reduce the computational time complexity. Based on the random attraction model, we propose a probabilistic attraction model. To test the performance of the proposed approach (PAFA), several benchmark functions are utilised in the experiments. Computational results show that the proposed strategy can effectively improve the performance of FA.