Improving Swarm Intelligence Accuracy with Cosine Functions for Evolved Bat Algorithm

The diversity created during the searching process in swarm intelligence algorithms plays an important part that affects the exploration ability. The searching result can be further improved if the algorithm gives consideration to both the exploitation and the exploration. In this paper, the evolved bat algorithm is improved by replacing the fixed value, which is determined by the media, with a cosine function. The familiar trigonometric signal exists in the natural environment is the sine/cosine signal. We take the cosine signal in our design for improving the searching capacity of the evolved bat algorithm. To verify the performance and the searching accuracy of our proposed strategy, three test functions with known global optimum values are used in the experiments. Moreover, every test function is tested with four different dimensional criteria, which include 10, 30, 50, and 100 dimensional test environments. The experimental results indicate that our proposed strategy improves the searching accuracy of the evolved bat algorithm about 28.098%, 48.779%, 45.945%, and 48.81%, respectively for different dimensional environments, in average.