Abstract
In this paper we present an extension to the Honey Bee Foraging PSO (HBF-PSO) algorithm. This algorithm is modeled after the food foraging behavior of the honey bees and performs a collective foraging for fitness in promising neighborhoods in combination with individual scouting searches in other areas. The strength of the algorithm lies in its continuous monitoring of the whole scouting and foraging process with dynamic relocation of the bees if more promising regions are found. The algorithm has the potential to be useful for optimization problems of multi-modal nature. The extension we propose allows HBF-PSO to automatically adjust the neighborhood size during execution which results in improved performance. The details of the algorithm are presented followed by experimental results on some commonly used multi-modal benchmark test functions. We present a comparison of both versions of HBF-PSO with NichePSO and SPSO.