Performance of Laplacian Biogeography-Based Optimization Algorithm on CEC 2014 continuous optimization benchmarks and camera calibration problem

This paper provides three innovations. Firstly, a new Laplacian BBO is presented which introduces a Laplacian migration operator based on the Laplace Crossover of Real Coded Genetic Algorithms. Secondly, the performance of the Laplacian BBO and Blended BBO is exhibited on the latest benchmark collection of CEC 2014. (To the best of the knowledge of the authors, the complete CEC 2014 benchmarks have not been solved by Blended BBO). On the basis of the criteria laid down in CEC 2014 as well as popular evaluation criteria called Performance Index, It is shown that Laplacian BBO outperforms Blended BBO in terms of error value defined in CEC 2014 benchmark collection. T-Test has also been employed to strengthen the fact that Laplacian BBO performs better than Blended BBO. The third innovation of the paper is the use of the proposed Laplacian BBO and Blended BBO to solve a real life problem from the field of Computer Vision. It is concluded that proposed Laplacian BBO is an efficient and reliable algorithm for solving not only the continuous functions but also real life problems like camera calibration.