MISO: mixed-integer surrogate optimization framework

Müller, Juliane

doi:10.1007/s11081-015-9281-2

Cited by 68 publications

(47 citation statements)

References 29 publications

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“…This process is conducted iteratively until a stopping criterion is met, and the best design point is chosen. In this work, the mixed-integer optimization problem is solved with SBO based on the work of Müller [55].…”

Section: A Brief Review Of Surrogate-based Optimizationmentioning

confidence: 99%

Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

et al. 2018

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Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

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Section: A Brief Review Of Surrogate-based Optimizationmentioning

confidence: 99%

Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

et al. 2018

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“…A very recent development in the field of surrogate based optimization for mixed integer type problems is by Muller. 21 The proposed framework uses a similar approach of identifying an infill point that satisfies the integrality constraint, however our approach is more suited for applications with large number of design variables.…”

Section: Surrogate Based Optimizationmentioning

confidence: 99%

A Mixed Integer Efficient Global Optimization Algorithm for the Simultaneous Aircraft Allocation-Mission-Design Problem

Roy

Moore

Hwang

et al. 2017

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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Aircraft design optimization and airline allocation problems are two separate and wellresearched disciplines, but very little literature exists that solved the design and allocation problems simultaneously. Among the limited number of related efforts that combine them, most follow a sequential decomposition strategy. This sequential strategy has been successful in addressing the combined large-scale problem but the approach does not capture the coupling that exists between the aircraft design and airline allocation disciplines. Solving the aircraft design and airline allocation as a monolithic problem makes it a Mixed Integer Non-Linear Programming problem which is very difficult to solve for large numbers of integer variables. Because no existing generalized MINLP solver can address this problem, this work proposes a new algorithm combining branch and bound, Efficient Global Optimization, Kriging Partial Least Squares, and gradient-based optimization to solve MINLP problems with 100's of integer design variables, 1000's of continuous design variables. The algorithm was applied to an 8 route coupled aircraft design and allocation problem with the 19 allocation variables and solving a 6000 variable aircraft design optimization problem using an Euler CFD simulation. This test problem provides several key challenges for a MINLP problem: a moderate integer design space, a large continuous design space, and expensive analysis models.

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“…As shown in Figure 2, the dynamic Kriging model adopts dynamic adding design point algorithm (DADPA), in which new sample points will be iteratively selected to do the expensive function simulation and update the model until the stopping criterion is met while the whole search space will not be changed. According to [39], DADPA has good performance in optimization problem. Hypercube Designs (LHD).…”

Section: Kriging Model Based Optimizationmentioning

confidence: 99%

A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement

Wang

Tang

et al. 2017

Energies

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Optimal operation of the active distribution networks (ADN) is essential to keep its safety, reliability and economy. With the integration of multiple controllable resources, the distribution networks are facing more challenges in which the optimization strategy is the key. This paper establishes the optimal operation model of the ADN considering a diversity of controllable resources including energy storage devices, distributed generators, voltage regulators and switchable capacitor banks. The objective functions contain reducing the power losses and improving the voltage profiles. To solve the optimization problem, the Kriging model based Improved Surrogate Optimization-Mixed-Integer (ISO-MI) algorithm is proposed in this paper. The Kriging model is applied to approximate the complicated distribution networks, which speeds up the solving process. Finally, the accuracy of the Kriging model is validated and the efficiency among the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) is compared in an unbalanced IEEE-123 nodes test feeder. The results demonstrate that the proposed method has better performance than GA and PSO.

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MISO: mixed-integer surrogate optimization framework

Cited by 68 publications

References 29 publications

Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

A Mixed Integer Efficient Global Optimization Algorithm for the Simultaneous Aircraft Allocation-Mission-Design Problem

A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement

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