Tabassum, Muhammad FarhanChaudhry, Nazir AhmadAkgül, AliFarman, MuhammadAkram, Sana2024-12-242024-12-2420222164-6376https://doi.org10.5890/DNC.2022.03.014https://hdl.handle.net/20.500.12604/3975Optimization for all disciplines is very important and relevant. Optimization has played a key role in the design and operation of industrial reactors, 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 (DE), Gradient Evolution (GE) and Jumping Technique (+) named as 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 DGE+ is investigated in thirteen benchmark unconstraint functions and the results are compared to the other state-of-the-art metaheuristics. The comparison shows that the proposed algorithm is able to outperform the other state-of-the-art meta-heuristics in almost all benchmark functions. To evaluate the efficiency of the DGE+ it has also been applied to complex constrained non-linear chemical design problems such as optimal operation of alkylation unit, reactor network design, optimal design of heat exchanger network, optimization of an isothermal continuous stirred tank reactor, the results of comparison revealed that the proposed algorithm is able to provide very compact, competitive and promising performance. © 2022. L&H Scientific Publishing, LLC. All Rights Reserved.eninfo:eu-repo/semantics/closedAccessMeta-heuristics Hybridization Differential evolution Gradient evolution Constrained optimization Benchmark test functions Chemical engineering design problemsArticle111161181Q32-s2.0-8512452249510.5890/DNC.2022.03.014