Two enhanced Differential Evolution variants for solving global optimization problems

Differential Evolution (DE) algorithms are very robust, effective and highly efficient in solving the global optimization problems. Thus, they are usually able to mitigate the drawback of long computation times commonly associated with Evolutionary algorithms. However, in certain cases the performance of DE is observed not to be completely flawless. In this paper we have proposed the two enhanced variants of DE using a modified mutation operator. The DE versions named as EDE-1 and EDE-2 are tested on six benchmark problems and a real time molecular potential energy problem. The simulation results prove the efficiency as well as the effectiveness of the proposed variants.