Adaptive Differential Evolution (ADE) for optimization of Non-linear chemical processes

Differential Evolution algorithm (DE), one of the evolutionary algorithms, is a novel optimization method capable of handling non-differentiable, non-linear and multimodal objective functions. DE takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification on original DE that enhances the convergence rate. Our Adaptive Differential Evolution algorithm (ADE) uses variable scaling parameter (F) as against constant scaling parameter in original DE at any iteration. Some functions such as logarithmic, exponential, inverse and square for changing F with iteration are examined, and Numerical results suggest that square function has a best performance to reduce solution vectors dispersal and results in faster convergence. The proposed ADE is applied to optimize three non-linear chemical engineering problems. Results obtained are compared with those obtained using DE by considering the convergence history (CPU time and the number of runs converged to global optimum) and error in any iteration. As compared to DE, ADE is found to perform better in locating the global optimal solution, reduces the memory and computational efforts by reducing the number of iteration to reach a global optimal solution for all the considered problems.

Keywords:

Global optimization , Evolutionary algorithm , Differential Evolution , Adaptive Differential Evolution , variable scaling parameter