An Improved Differential Evolution Algorithm for Real Parameter Optimization Problems

Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This paper introduces an improved differential evolution (IDE), a modification to DE that enhances the convergence rate without compromising with the solution quality. In improved differential evolution (IDE) algorithm, initial population of individual is partitioned into several sub-populations, and then DE algorithm which utilize only one set of population instead of two as in original DE, is applied to each sub-population independently. At periodic stages in evolution, the entire population is shuffled, and then points are reassigned to subpopulations. The performance of IDE on a test bed of functions is compared with original DE. It is found that IDE requires less computational effort to locate global optimal solution.