A New Approach for Low-Dimensional Constrained Engineering Design Optimization Using Design and Analysis of Simulation Experiments

Abstract

The number of function evaluations in many industrial applications of simulation-based optimization problems is strictly limited. Therefore, only little analytical information on objective and constraint functions is available. This paper presents an adaptive algorithm called the Surrogate-Based Constrained Global-Optimization (SCGO) method to solve black-box constrained simulation-based optimization problems involving computationally expensive objective function and inequality constraints. Firstly, Kriging surrogate is constructed over a new overall objective function (called loss function) to approximate the behavior of a true model. Then, an adaptive approach is provided to improve the optimal results sequentially while enforcing a feasible solution. The SCGO method is tested on several classical engineering design problems namely design of a tension/compression spring, design of a welded beam, design of a pressure vessel, and three-bar truss design. The results demonstrate that SCGO has advantages in solving the costly constrained problems and needs less costly function evaluations. Optimization results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms.

A data driven approach in less expensive robust transmitting coverage and power optimization

“…Thus, in a multi-transmitter placement problem particularly under uncertainty, directly applying metaheuristics such as evolutionary algorithm as used in 1 , 18 , 25 , 83 or swarm-intelligence as applied in 24 , 27 imposes high computational cost due to a large number of function evaluations. These techniques that require a large number of fitness evaluations to obtain robustness besides accuracy (lower objective function) are often limited to directly being applied to computationally expensive engineering problems under uncertainty, therefore, surrogate-assisted metaheuristic optimization algorithms have been proposed in the literature, see 84 – 86 .…”

Digital-Twins Towards Cyber-Physical Systems: A Brief Survey

“…Since digital twins frequently employ intricate mathematical models, it is difficult to apply efficient optimization techniques, such as non-linear multiresponses constrained optimization [74]- [77], real-time intelligent control [7], [78]- [81], robust uncertainty management [39], [82], due to their high processing requirements. Most simulations used in real-world DT require a lot of computational costs to assess the various unknown model functions [83].…”

Constrained optimization in simulation: efficient global optimization and Karush-Kuhn-Tucker conditions

“…Chaiyotha and Krityakierne (2020) compares several EGO-based methods for solving a pressure vessel design optimization problem. Parnianifard, et al (2020) applies a new method to four real-world engineering design problems; namely, tension/compression spring, pressure vessel designs, welded beam, and three-bar truss design. Priem et al ( 2020) applies a new method to optimize a conceptual aircraft con…guration.…”

“…Several more methods are discussed in Bagheri et al (2017b), Cheng et al (2018), Durantin et al (2016), Dzahini et al (2020), Habib et al (2016), Friese et al (2020), Haftka et al (2016), Müller and Woodbury (2017), Parnianifard, et al (2020), Passos et al (2019), Lee (2021), Su et al (2020), and Ungredda and Branke (2021).…”

“…Wu et al (2021) develops a method including EGO and PI, and low-…delity combined with high-…delity simulation models. Parnianifard, et al (2020) uses a loss function that combines the objective and the constraints of the optimization model with no more than four inputs, and uses a one-shot LHS design with 30 or 100 combinations to estimate a Kriging metamodel to this loss function. Cheng et al (2018) uses the so-called "Kreisselmeier and Steinhauser function"which aggregates the constrained simulation outputs using some prespeci…ed parameter-but uses RBFs instead of Kriging.…”