Bat algorithm with Gaussian walk
Xingjuan Cai, Lei Wang, Qi Kang, Qidi Wu · International Journal of Bio-Inspired Computation · 2014
Bat algorithm is a novel branch of evolutionary computation. Although there are several research papers that focus on this new algorithm, however, few of them concerns the high-dimensional numerical problems. In this paper, a new variant called bat algorithm with Gaussian walk (BAGW) is proposed aiming to solve this problem. In this variant, a Gaussian walk is employed in the local turbulence instead of the original uniform walk to improve the local search capability. Furthermore, to keep the high exploitation pressure, the velocity update equation is also changed. Finally, to increase the population diversity, the frequency is dominated by each dimension in our modification, as well as it is depended on the different bat in the standard version. To test the performance of our variant, four famous un-constraint numerical benchmarks are employed, and test on different dimensional cases, simulation results show our modification is effective.