Enhancing the firefly algorithm through a cooperative coevolutionary approach: an empirical study on benchmark optimisation problems Online publication date: Sat, 27-Sep-2014
by Giuseppe A. Trunfio
International Journal of Bio-Inspired Computation (IJBIC), Vol. 6, No. 2, 2014
Abstract: In recent years, the firefly algorithm (FA) has been applied with success to many classes of optimisation problems. However, as is the case for all metaheuristic optimisation algorithms, also with FA can be observed a rapid deterioration of efficiency as the dimensionality of the search space increases. In this paper, we use a cooperative coevolutionary approach for enhancing FA with the aim of making it much more efficient in the case of search spaces with many dimensions. We assess the performance of the cooperative coevolutionary firefly algorithm (CCFA) through a computational study based on some significant benchmark functions with up to 1,000 dimensions. Moreover, we compare the proposed CCFA with two state-of-the-art algorithms for high-dimensional optimisation problems. According to our results, CCFA can lead to significantly improved solutions in comparison to the standard FA. In addition, we show that the CCFA computation time is significantly lower than that of FA.
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