Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution
For researchers using the bat algorithm, this provides theoretical guidance on parameter settings, though the analysis is algorithm-specific and incremental.
The authors use dynamical systems theory and population variance evolution to derive theoretical parameter ranges for the bat algorithm, and show that numerical experiments align with these bounds.
Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical experiments to show that the algorithm can work well in practice; however, in most cases there is no theoretical analysis of parameter settings. In this work, we show that theoretical analysis using the theory of dynamical systems and evolution of population variance can give some good results in terms of parameter ranges for the bat algorithm. We also show that results from numerical experiments are consistent with theoretical bounds. Such analyses can provide good insights from different perspectives about the algorithmic characteristics such as variance evolution, transition between exploration and exploitation as well as convergence behaviour.