An Improved BAT Algorithm Using Density-Based Clustering

Abstract

BAT algorithm is a nature-inspired metaheuristic algorithm that depends on the principle of the echolocation behavior of bats. However, the algorithm suffers from being stuck in the local optima early due to its poor exploration. An improved BAT algorithm based on the density-based clustering technique is proposed to enhance the algorithm’s performance.

In this paper, the initial population is improved by generating two populations, randomly and depending on the clusters’ center information, and by getting the fittest individuals from these two populations, the initial improved one is generated. The random walk function is improved using chaotic maps instead of the fixed-size movement, and so the local search is improved as well as the global search abilities by diversifying the solutions. Another improvement is to deal with stagnation by partitioning the search space into two parts depending on the generated clusters’ information to obtain the newly generated solution and comparing their quality with the previously generated solution and choosing the best.

The performance of the proposed improved BAT algorithm is evaluated by comparing it with the original BAT algorithm over ten benchmark optimization test functions. Depending on the results, the improved BAT outperforms the original BAT by obtaining the optimal global solutions for most of the benchmark test functions.