Process Systems/Product Design ChemCon’04 Mumbai Optimization Using Hybrid Differential Evolution Algorithms

In recent years, evolutionary algorithms (EAs) have been used for the solution of nonlinear multimodal problems encountered in many engineering disciplines. They differ from the traditional gradient based algorithms since, in general, only the information regarding the objective function is required. In the present study, a hybrid evolutionary algorithm is proposed. This new method is a combination of Differential Evolution (DE) and classical Quasi-Newton method and named as Hybrid Differential Evolution (HDE). HDE is used to solve the benchmark test functions and then evaluated for water pumping system. Performance of HDE is compared with DE. The results show that both DE and HDE have high reliability in locating the global minimum, and that HDE converges faster than DE thus reducing the computational time and number of function evaluations.