The real-world design optimization problems are complex and multidisciplinary. For example, design of a satellite system involves complex interactions between various sub-systems with a large number of design variables and constraints that limits application of conventional design optimization methods to this class of problems. In addition, design search space of such problems can be large and non-convex involving simulation of several interacting disciplines and hence it is time consuming or difficult to rapidly evaluate trade-offs between various disciplines. To address these difficulties, several research works are focused on the multidisciplinary design optimization methods. In this respect, this paper presents and efficient multidisciplinary design optimization framework using metamodels. The proposed method extends multidiscipline feasible concept using metamodels, design of experiments (DoE) and sequential quadratic programming (SQP) for solving large scale design optimization problems such as satellite systems. The proposed method is compared with the existing methods using a number of benchmark problems. The proposed method is implemented on a remote sensing satellite system. The results obtained show that the proposed method provides an effective way of solving large-scale design optimization problems.

One of the ways to prevent creating negative pressure and cavitation in spillways is to introduce air into the flow over the spillways. Understanding the distribution of air concentration variations along the spillway is of significant importance for estimating the aeration level. This study explores the application of GPR and SVM molels in predicting air concentration. To achieve this, a dataset of 2268 laboratory experiments obtained from hydraulic models of chute spillways was utilized in the modeling process. Various input models were defined based on different combinations of measured parameters. The results demonstrate the high capability of both methods in estimating the required air concentration over the spillway. In predicting air concentration in the chute spillway under artificial aeration conditions, flow discharge (QW), longitudinal distance ratio from the end of the deflector to the channel width (L/W), and depth ratio (perpendicular to the spillway) to channel width (Y/W) significantly influenced the outcomes. Statistical indices, including R, DC, and RMSE for this case were 0.9214, 0.8451, and 1.008, respectively, in the GPR, and 0.9333, 0.8662, and 0.937 in the SVM. For scenarios without artificial aeration, the model with input parameters QW, L/W, Y/W, and ΔP (pressure difference between atmospheric pressure and the pressure under the jet) achieved the best performance in the GPR method with values of R=0.9222, DC=0.8644, and RMSE=0.914. In the SVM, the same model with values of 0.87, 0.7543, and 0.123 for R, DC, and RMSE, respectively, was selected as the superior model.

An aeroelastic metamodel was designed and implemented for prediction of flutter speed and frequency of swept rectangular wings based on experimental data and artificial neural networks (ANN). The ANN is a supervised multilayer perceptron that was trained based on an experimental data set involves flutter characteristics of various cantilever rectangular wing models. Some data were not learned to ANN and were maintained as test cases. The activation functions were tangent hyperbolic and linear function in the hidden and output layers respectively. For learning process, the normalized form of the inputs and outputs were given to the ANN. The ANN learned the relation between the inputs and outputs and was trained for predicting output parameters. It is observed that ANN results are in good agreement with experimental data as well as results of an aeroelasticity code developed using an analytical aerodynamic model. So this ANN can be used for quick prediction of flutter characteristics of swept rectangular wings and also for the study of the effects of various parameters on flutter characteristics of swept rectangular cantilevered wings.

This paper introduces a novel two-phase metamodel-driven methodology for the simultaneous topology and size optimization of truss structures. The approach addresses critical limitations in computational efficiency and solution quality. The framework integrates the Flexible Stochastic Gradient Optimizer (FSGO) with adaptive sampling and machine learning to minimize the number of structural analyses (NSAs), while achieving lighter, high-performance designs. In Phase One, FSGO employs a dual global-local search strategy governed by Extensive Constraints (EC), a dynamic constraint relaxation mechanism to balance exploration of unconventional topologies and exploitation of optimal member sizes. By creating adaptive margins around design constraints, EC enables broader exploration of the design space while ensuring feasibility. Phase Two focuses on precision size optimization, leveraging pruned metamodels trained on critical regions of the design space to refine cross-sectional areas for the finalized topology. Comparative evaluations on benchmark planar and spatial trusses demonstrate the method’s superiority: it reduces NSAs by 22–79% compared to state-of-the-art approaches and achieves 0.04–0.7% lighter designs while eliminating up to 31% of redundant members. Results validate the framework as a paradigm shift in truss optimization, merging computational efficiency with structural innovation.

Although many problems in the literature involve complex mathematical relationships, many still rely on simplified and unrealistic assumptions. Simulation is one of the most powerful tools for dealing with such problems, as it avoids the restrictive assumptions often required in stochastic systems. Simulation optimization techniques are generally classified into two broad categories: model-based and metamodel-based methods. In the first category, simulation and optimization components interact directly, thereby increasing simulation time and cost. To address this issue, a third component—called a metamodel—is introduced in the second category to estimate the system's relationship between input and output variables. Optimizing semi-expensive simulation problems often requires many simulation runs in model-based methods. However, the cost of validating metamodels also rises rapidly during iterations. A two-phase method has been proposed in the literature to reduce computation time. In the first phase, similar to a model-based algorithm, the simulation output is used directly in the optimization process. In the second phase, a validated metamodel replaces the simulation model. In this paper, an artificial neural network (ANN) is employed as the metamodel, and its performance is compared with that of the original algorithm, which employs a Kriging metamodel, on five well-known test functions and an (s, S) inventory model.

Grain design is the most important part of a solid rocket motor. The aim of this study is finocyl grain design based on predetermined objective function with respect to ballistic curves in order to satisfy various thrust performance requirements through an innovative design approach using a genetic algorithm optimization method. The classical sampling method has been used for design space-filling. The level set method has been used for simulating the evolution of the burning surface in the propellant grain. An algorithm has been developed beside the level set code that prepares the initial grain configuration using Pro/Engineer software to export generated models to level set code. The lumped method has been used to perform internal ballistic analysis. Two meta-models are used to surrogate the level set method in the optimization design loop. The first method is based on adaptive basis function construction and the second method is based on the artificial neural network. In order to validate the proposed algorithm, a grain finosyl sample has been investigated. The results show that both grain design method reduced the design time significantly and this algorithm can be used in designing of any grain configuration. In addition, data have more accuracy in grain design based on the artificial neural network, so this method is the more effective and practical method to grain burn-back training.

Nowadays, achieving desirable and stable security in networks with national and organizational scope and even in sensitive information systems, should be based on a systematic and comprehensive method and should be done step by step. Cryptography is the most important mechanism for securing information. a cryptographic system consists of three main components: cryptographic algorithms, cryptographic keys, and security protocols, which are mainly based on cryptographic algorithms. In designing a cryptographic algorithm, all the necessary components of information security must be considered in a model of excellence in technical, organizational, procedural and human aspects. To meet these needs, we must first extract the effective components in the design and implementation of cryptographic algorithms based on a model and then determine the impact of the components. In this paper, we use cybernetic methodology to prepare a metamodel. The cryptographic cybernetics metamodel has four components: " strategy / policy ", "main process", "support process" and "control process". The "main process" has four stages and also, the "suport process" includes 13 components of hardware and software. The interactions of these two processes shape its structure, leading to a complex graph. To prioritize suport components for resource allocation and cryptography strategy, it is necessary to rank these components in the designed metamodel. To overcome this complexity in order to rank the support components, we use the ELECTRE III method, which is a multi-criteria decision-making method. The results show that the components with high priority for the development of the cryptographic system are: Research and Development, Human Resources, Management, Organizational, Information and Communication Technology, Rrules and Regulations and standards. These results are consistent with reports published by the ITU in 2015, 2017 and 2018.

Mathematical modeling is a powerful tool for prediction of Ion Sensitive Field Effect Trnsistor (ISFET) response and optimization of its functional parameters. In this study the optimal values of drain current, drain voltage and initial concentrations of substrate and enzyme parameters were determined to achieve maximum of ISFET response for detection of Aflatoxin B1 (AFB1). Optimization was performed by using Genetic Algorithm (GA) and based on numerical solution of ISFET governing differential equations by means of Finite Element Method (FEM) and COMSOL Multiphysics software. The objective function of GA was defined through substituting simulated model by Artificial Neural Network (ANN) metamodel. The results showed that ISFET simulated FEM model has a MAPE equal to 1.06 % in prediction of ISFET response compared with experimental results. With FEM model, 1296 virtual experiments were simulated to achieve necessary data base for train ANN metamodel. By evaluation of different ANN structures, trained ANN with 4-45-1 structure was selected which has MAPE equal to 0.04 %, 0.07% and 0.05% at train, validation and test phase respectively. ISFET optimization results states that by using of GA determined optimal values of drain current, drain voltage and initial concentrations of substrate and enzyme parameters, extremum response of ISFET equal to 44.44 % was achieved.