  The experiments conducted in previous studies demonstrated the successful
performance of BSA and its non-sensitivity toward the several types of
optimisation problems. This success of BSA motivated researchers to work on
expanding it, e.g., developing its improved versions or employing it for
different applications and problem domains. However, there is a lack of
literature review on BSA; therefore, reviewing the aforementioned modifications
and applications systematically will aid further development of the algorithm.
This paper provides a systematic review and meta-analysis that emphasise on
reviewing the related studies and recent developments on BSA. Hence, the
objectives of this work are two-fold: (i) First, two frameworks for depicting
the main extensions and the uses of BSA are proposed. The first framework is a
general framework to depict the main extensions of BSA, whereas the second is
an operational framework to present the expansion procedures of BSA to guide
the researchers who are working on improving it. (ii) Second, the experiments
conducted in this study fairly compare the analytical performance of BSA with
four other competitive algorithms: differential evolution (DE), particle swarm
optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16
different hardness scores of the benchmark functions with different initial
control parameters such as problem dimensions and search space. The
experimental results indicate that BSA is statistically superior than the
aforementioned algorithms in solving different cohorts of numerical
optimisation problems such as problems with different levels of hardness score,
problem dimensions, and search spaces.
