Novel Hybrid Evolutionary Algorithm for Dynamic Optimization Problems and Its Application in an Ethylene Oxide Hydration Reactor

Abstract

A novel hybrid evolutionary algorithm (HEA) that combines the genetic algorithm (GA) and particle swarm optimization (PSO) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. Based on the characteristics of dynamic optimization problems, the concept of “search region reduction” is integrated into the HEA to improve the convergence rate. A control vector parametrization (CVP) method based on the HEA is also employed to improve the accuracy of the results. The dynamic optimization problem with state variable constraints is an important research area in process system engineering and is difficult to solve. Thus, the present work also proposes a novel method embedding information about infeasible chromosomes into the evaluation function to solve dynamic optimization problems with or without state variable constraints. The results of several case studies demonstrate the feasibility and efficiency of the proposed methods. Finally, the proposed methods are used to solve the temperature distribution problem in an ethylene oxide hydration reactor. Moreover, the proposed algorithm can be regarded as a useful optimization tool, especially when gradient information is unavailable.