Solution of chemical dynamic optimization systems using novel differential gradient evolution algorithm

Abstract Optimization for all disciplines is essential and relevant. Optimization has played a vital role in industrial reactors’ design and operation, separation processes, heat exchangers, and complete plants in Chemical Engineering. In this paper, a novel hybrid meta-heuristic optimization algorithm which is based on Differential Evolution ( D E ) , Gradient Evolution ( G E ) , and Jumping Technique (+) named Differential Gradient Evolution Plus (DGE+) is presented. The main concept of this hybrid algorithm is to enhance its exploration and exploitation ability. The proposed algorithm hybridizes the above-mentioned algorithms with the help of an improvised dynamic probability distribution, additionally provides a new shake off method to avoid premature convergence towards local minima. The performance of D G E + is investigated in thirteen benchmark unconstraint functions, and the results are compared to the other state-of-the-art meta-heuristics. The comparison shows that the proposed algorithm can outperform the other state-of-the-art meta-heuristics in almost all benchmark functions. To evaluate the precision and robustness of the D G E + it has also been applied to complex chemical dynamic optimization systems such as optimization of a multimodal continuous stirred tank reactor, Lee-Ramirez bioreactor, Six-plate gas absorption tower, and optimal operation of alkylation unit, the results of comparison revealed that the proposed algorithm can provide very compact, competitive and promising performance overall complex non-linear chemical design problems.