The Mesh Adaptive Direct Search Algorithm for Granular and Discrete Variables

Audet, Charles; Digabel, Sébastien Le; Tribes, Christophe

doi:10.1137/18m1175872

Cited by 53 publications

(38 citation statements)

References 57 publications

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“…To verify the proposed GPR-AWMOPSO algorithm, the optimized proportion of asphalt mixture is compared with the results of RSM, traditional MOPSO [43][44][45] and meshadaptive direct search (MADS) [46][47][48]. When the objective is to achieve the maximum Marshall stability, the three inputs and five outputs variables are summarized in Table 5.…”

Section: No Preparation Parametersmentioning

confidence: 99%

Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

Liang

Chen

et al. 2021

Applied Sciences

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Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R2. After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.

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Section: No Preparation Parametersmentioning

confidence: 99%

Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

Liang

Chen

et al. 2021

Applied Sciences

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“…A simplex is a polytope with n+1 vertices, where n is the dimension of the search space. Since its introduction, this algorithm has been studied and recast in different forms, including methods to handle constraints and discrete grids [25,27].…”

Section: Optimization Toolsmentioning

confidence: 99%

Calibration of the SLEUTH urban simulation model using NOMAD and Genetic Algorithms

Koscianski¹,

Amaral²

2021

IJAERS

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Computer simulations often entail an optimization problem, corresponding to the calibration of model parameters to ensure a precise representation of a given scenario. Many complex phenomena such as urban growth have characteristics that make optimization harder; this can be exemplified by the lack of an analytical formulation, presence of nonlinearity, discontinuities, and nondeterminism. SLEUTH is a long-established urban simulator, used to compute forecasts of city evolution. The tool is controlled by five parameters that span a search space of the order of 10 billion combinations, with a calibration procedure that is CPU-intensive and not compatible with gradient-descent methods. In this work we compare the efficiency of a genetic-algorithm version of the simulator with the use of the optimization library NOMAD. Different alternatives for the integration of the library are suggested. The experiments are analyzed using data profiles, a technique designed to handle cases with a limited number of function evaluations. The results confirm interest in NOMAD, and reveal more information than traditional comparisons of number of iterations or final optimization results. The methodology of the study can be applied to similar situations and is not restricted to simulators implementing urban models.

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“…Because the stator-and rotor-blade pitch angles constitute the 2D design space, x is now restricted to a 2D integer grid. This, in turn, affects the LHS algorithm, the CMA-ES and the associated step-size adaptation of the search step as outlined in Hansen [10], and necessitates the introduction of a rounding function in the MADS-based polling technique as suggested in Audet et al [66]. These modifications ensure that the LHS together with the search and poll steps only produce integer values as outputs, and thus constrain the optimizer to only examine integer stator-and rotor-blade positions.…”

Section: Smf Modifications For Vsgte Optimizationmentioning

confidence: 99%

Optimizing Gas Turbine Performance Using the Surrogate Management Framework and High-Fidelity Flow Modeling

Kozak

Rajanna

et al. 2020

Energies

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This work couples high-fidelity moving-domain finite element compressible flow modeling with a Surrogate Management Framework (SMF) for optimization to effectively design a variable speed gas turbine stage. The superior accuracy of high-fidelity modeling, however, comes with relatively high computational costs, which are further amplified in the iterative design process that relies on parametric sweeps. An innovative approach is developed to reduce the number of iterations needed for optimal design, leading to a significant reduction in the computational cost without sacrificing the high fidelity of the analysis. The proposed design optimization approach is applied to a novel incidence-tolerant turbomachinery blade technology that articulates the stator- and rotor-blade positions of an annular single-stage high pressure turbine to achieve peak performance. This work also extends our understanding of rotor–stator interactions by simulating complex internal flows occurring during multi-speed turbine operation. Potential variable-speed gas turbine stage designs and the proposed optimization approach are presented to provide valuable insight into this new turbomachinery technology that can positively impact future propulsion systems.

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The Mesh Adaptive Direct Search Algorithm for Granular and Discrete Variables

Cited by 53 publications

References 57 publications

Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

Calibration of the SLEUTH urban simulation model using NOMAD and Genetic Algorithms

Optimizing Gas Turbine Performance Using the Surrogate Management Framework and High-Fidelity Flow Modeling

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