Applications of genetic algorithms in chemical engineering I: Methodology

Abstract

The fascinating world of genes has been an inspiration for mankind. One such inspiration has led to a popular optimization technique, genetic algorithm (GA). Its inherent parallelism has enabled significant computational improvement over deterministic enumerations. Further, it has provided a flexibility of solving multiple objectives in a derivative-free environment. These advantages are extremely useful for solving optimization problems in chemical engineering, ranging over a wide variety of processes from the production of bulk chemicals to highly sophisticated specialty chemicals, their purification, control, planning, and scheduling. These systems are often associated with multiple objectives and complex model equations. Several variations of GA have been developed over the last four decades by incorporating ground-breaking concepts such as elitism, jumping gene, crowding distance, ranking, altruism, etc., to enable faster convergence of the algorithms. Continuous improvements are being made by the use of new or hybrid concepts so as to provide improved applicability and flexibility, and so as to exploit the rapidly increasing computational speeds.