Evaluating the Performance of "Derandomized-LShade" Algorithm on CEC 2014 Benchmark Functions

In this paper, a hybrid global optimization algorithm is proposed. The new algorithm Derandomized-LShade (DL-Shade), exploits derandomization characteristics of the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to enhance the performance of L-SHADE algorithm. The performance of DL-SHADE has been evaluated on the benchmark functions of CEC2014 Competition. Also, DL-Shade efficiency has been compared with L-SHADE and CMA-ES. Comparison results show that the proposed algorithm takes the good features from both algorithm and it was able to outperform both of them.