A novel hybrid Genetic Algorithm for HEN synthesis and its industrial application

In this paper we look at a new hybrid Genetic Algorithm (HGA) based on genetic simulated annealing (GSA) algorithm for solving heat exchanger network synthesis (HENS) problems with Mixed Integer Nonlinear Programming (MINLP) model. In order to efficiently locate quality solution to complex optimization problem, a self-adaptive mechanism is developed to maintain a tradeoff between the global and local search. The computational results indicate that the global searching ability and the convergence speed of this hybrid algorithm are significantly improved. Further, the proposed algorithm is tailored to find optimum solution to industrial HENS problem, The results show that the proposed approach could provide designers with a least-cost HEN with less computational cost comparing with other optimization methods.