A robust parallel adaptive genetic simulated annealing algorithm and its application in process synthesis

A robust hybrid genetic algorithm which can be used to solve process synthesis problems with Mixed Integer Nonlinear Programming (MINLP) models is developed. The proposed hybrid approach constructs an efficient genetic simulated annealing (GSA) algorithm for global search, while the iterative hill climbing (IHC) method as a local search technique is incorporated into GSA loop to speed up the convergence of the algorithm. 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 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.