Differential Evolution using Quadratic Interpolation for Initializing the Population

The performance of population based search techniques like Differential Evolution (DE) depends largely on the selection of initial population. A good initialization scheme not only helps in giving a better final solution but also helps in improving the convergence rate of the algorithm. In the present study we propose a novel initialization scheme which uses the concept of quadratic interpolation to generate the initial population. The proposed DE is validated on a test bed of 10 benchmark problems with varying dimensions and the results are compared with the classical DE using random initialization, DE using opposition based learning for generating the initial population. The numerical results show that the proposed algorithm using quadratic interpolation for generating the initial population accelerates the convergence speed quite considerably.