A Modified Bat Algorithm Based on Gaussian Distribution for Solving Optimization Problem

An improved Bat algorithm with Gaussian distribution random walk (BAGD) is introduced in this paper. The original Bat algorithm has a problem of random large step length that leads to sub-optimal solutions in the search space and it cannot solve higher dimensional problems. To solve higher dimensional problems and to decrease the step length size, this research focuses on using a Gaussian distribution in Bat algorithm which provide shorter step lengths during the search. The proposed BAGD was compared with six popular metaheuristic algorithms on ten benchmark functions. Comparative results indicated that the proposed BAGD perform better than the state-of-the-art algorithms in most cases. The proposed BAGD solution used small step lengths in the search space and it was able to solve high dimensional problems.