Developing Adaptive Differential Evolution As A New Evolutionary Algorithm, Application in Optimization of Chemical Processes

Differential evolution algorithm (de), one of the evolutionary algorithms, is a new optimization technique capable of handling non-differentiable, non-linear and multimodal objective functions. de needs a large run time for optimizing the complex objective function. thus, an attempt to speed up de is necessary. this paper introduces a modification on original de that enhances the convergence rate by reducing vector dispersal at any iteration. our adaptive differential evolution algorithm (ade) utilizes variable scaling parameter (f) against constant scaling parameter in original de at any iteration. the proposed ade is applied to optimize three non-linear chemical engineering problems. the obtained results have been compared with those results obtained using de. the considered comparison criteria are the vectors dispersal, convergence history (run time and number of iterations that led to reach 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 iterations required to reach the global optimal solution for all the considered problems.