Modified Mutation Operators for Differential Evolution

Differential Evolution (DE) has emerged as a popular tool for solving global optimization problems occurring in various fields. However, like most of the population based random search techniques in their basic form, DE has some inherent drawbacks like slow and/or premature convergence, stagnation etc. which sometimes hinders its performance. In the present study we propose two new mutation schemes for DE to enhance its performance in terms of solution quality as well as convergence rate.