An Improved Bees Algorithm for Real Parameter Optimization

Abstract

The Bees Algorithm (BA) is a bee swarm-based search algorithm inspired by the foraging behavior of a swarm of honeybees. BA can be divided into four parts: the parameter tuning part, the initialization part, the local search part, and the global search part. Recently, BA based on Patch-Levy-based Initialization Algorithm (PLIA-BA) has been proposed. However, the initial stage remains an initial step, and its improvement is not enough for more challenging problem classes with different properties. The local and global search capabilities are also required to be enhanced to improve the quality of final solution and the convergence speed of PLIA-BA on such problems. Consequently, in this paper, a new local search algorithm has been adopted based on the Levy looping flights. Moreover, the mechanism of the global search has been enhanced to be closer to nature and based on the patch-Levy model adopted in the initialization algorithm (PLIA). The improvements in local and global search parts are incorporated into PLIA-BA to advise a new version of BA that is called Patch-Levy-based Bees Algorithm (PLBA). We investigate the performance of the proposed PLBA on a set of challenging benchmark functions. The results of the experiments indicate that PLBA significantly outperforms the other BA variants, including PLIA-BA and can produce comparable results with other state-of-the-art algorithms.