The main aim of this paper is to present a novel multi-objective gray wolf optimization (MOGWO) by utilizing the Kriging meta-model. The meta-model is obtained based on exact analysis and numerical simulations. | A kriging based multi objective gray wolf optimization for hydrazine catalyst bed Engineering Solid Mechanics 7 2019 179-192 Contents lists available at GrowingScience Engineering Solid Mechanics homepage esm A kriging based multi objective gray wolf optimization for hydrazine catalyst bed M. N. P. Meibodya H. Naseha and F. Ommib a Aerospace Research Institute Ministry of Science Research and Technology Tehran Iran b Department of Mechanical Engineering Tarbiat Modares University Tehran Iran A R T I C L EI N F O ABSTRACT Article history The main aim of this paper is to present a novel multi-objective gray wolf optimization MOGWO Received 20 December 2018 by utilizing the Kriging meta-model. To this end surrogate models are used in Multi-Objective Accepted 29 May 2019 Gray Wolf Optimizer as the fitness function. The meta-model is obtained based on exact analysis Available online and numerical simulations. Inheritable Latin Hypercube Design ILHD is used as the design of 29 May 2019 Keywords experiments for generation and testing the Kriging model. Then sensitivity analysis is done to Multi-objective Optimization evaluate the effect of design parameter on system responses. The sensitivity analysis leads to Catalyst bed appropriate selection of optimization design variables. Hence the MOGWO algorithm is applied Meta-model to the problem the set of non-dominated optimal points are obtained as Pareto Front and one Gray Wolf Optimization optimal point is selected based on the minimum distance approach. The most important purpose of Kriging the methodology is to improve the time consuming in multi-objective optimization problems. In conclusion for the design of hydrazine catalyst bed was utilized from the proposed methodology. In case design variables are catalyst bed pellet diameter loading factor thrust chamber pressure and Reaction efficiency and objective functions are increasing performance and reducing mass and pressure drop. The results of .
