Optimization of process synthesis and design problems: A modified differential evolution approach

A large number of process synthesis and design problems in chemical engineering can be modeled as mixed integer nonlinear programming (MINLP) problems. They involve continuous (floating point) and integer variables. A common feature of this class of mathematical problems is the potential existence of non-convexities due to the particular form of the objective function and/or the set of constraints. Due to their combinatorial nature, these problems are considered to be difficult. In recent years, evolutionary algorithms (EAs) are gaining popularity for finding the optimal solution of nonlinear multimodal problems encountered in many engineering disciplines. In the present study, a novel modified differential evolution [Angira, R., Babu, B.V., 2005a. Optimization of non-linear chemical processes using modified differential evolution (MDE). Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI-05), Pune, India, December 20–22, pp. 911–923. Also available at 〈〈http://discovery.bits-pilani.ac.in/discipline/chemical/bvb/publications.html〉〉], one of the evolutionary algorithms, is used for solving process synthesis and design problems. To illustrate the applicability and efficiency of modified differential evolution (MDE), seven test problems on process synthesis and design have been solved. These problems arise from the area of chemical engineering, and represent difficult nonconvex optimization problems, with continuous and discrete variables. The performance of MDE is compared with that of Genetic Algorithm, Evolution Strategy, and MINLP-Simplex Simulated Annealing (M-SIMPSA).