On a novel multi-swarm fruit fly optimization algorithm and its application

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Recently, a kind of Drosophila (fruit fly) inspired optimization algorithm, called fruit fly optimization algorithm (FOA), has been developed. This paper presents a variation on original FOA technique, named multi-swarm fruit fly optimization algorithm (MFOA), employing multi-swarm behavior to significantly improve the performance. In the MFOA approach, several sub-swarms moving independently in the search space with the aim of simultaneously exploring global optimal at the same time, and local behavior between sub-swarms are also considered. In addition, several other improvements for original FOA technique is also considered, such as: shrunk exploring radius using osphresis, and a new distance function. Application of the proposed MFOA approach on several benchmark functions and parameter identification of synchronous generator shows an effective improvement in its performance over original FOA technique.