Extreme Learning Surrogate Models in Multi-objective Optimization based on Decomposition

2016 ◽  
Vol 180 ◽  
pp. 55-67 ◽  
Author(s):  
Lucas M. Pavelski ◽  
Myriam R. Delgado ◽  
Carolina P. Almeida ◽  
Richard A. Gonçalves ◽  
Sandra M. Venske
Keyword(s):  
Surrogate Models ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Multi-objective optimization design for a battery pack of electric vehicle with surrogate models

2016 ◽  
Vol 18 (4) ◽  
pp. 2343-2358 ◽  
Author(s):  
Fengling Gao ◽  
Cheng Lin ◽  
Wenwei Wang ◽  
Xiaokai Chen
Keyword(s):  
Electric Vehicle ◽  
Optimization Design ◽  
Surrogate Models ◽  
Battery Pack ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 391 ◽  
Author(s):  
Chuankun Liu ◽  
Yue Hu ◽  
Ting Yu ◽  
Qiang Xu ◽  
Chaoqing Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Treatment ◽  
Surrogate Models ◽  
Water Quality Modeling ◽  
Support Vector ◽  
Practical Case ◽  
Multi Objective Optimization ◽  
Artificial Lake ◽  
Multi Objective ◽  
Engineering Costs

The tradeoff between engineering costs and water treatment of the artificial lake system has a significant effect on engineering decision-making. However, decision-makers have little access to scientific tools to balance engineering costs against corresponding water treatment. In this study, a framework integrating numerical modeling, surrogate models and multi-objective optimization is proposed. This framework was applied to a practical case in Chengdu, China. A water quality model (MIKE21) was developed, providing training datasets for surrogate modeling. The Artificial Neural Network (ANN) and Support Vector Machine (SVM) were utilized for training surrogate models. Both surrogate models were validated with the coefficient of determinations (R2) greater than 0.98. SVM performed more stably with limited training data sizes while ANN demonstrated higher accuracies with more training samples. The multi-objective optimization model was developed using the genetic algorithm, with targets of reducing both engineering costs and target aquatic pollutant concentrations. An optimal target concentration after treatment was identified, characterized by the ammonia concentration (1.3 mg/L) in the artificial lake. Furthermore, scenarios with varying water quality in the upstream river were evaluated. Given the assumption of deteriorated upstream water quality in the future, the optimal proportion of pre-treatment in the total costs is increasing.

Mixture Surrogate Models for Multi- Objective Optimization

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Shailesh S. Kadre ◽  
Vipin K. Tripathi
Keyword(s):  
Finite Element ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Multiple Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Costly Function ◽  
Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

Robust Design Optimization of a Steam Turbine Labyrinth Seal Based on Surrogate Models

2018 ◽  
Author(s):  
Kevin Cremanns ◽  
Dirk Roos ◽  
Simon Hecker ◽  
Andreas Penkner ◽  
Christian Musch
Keyword(s):  
Power Plants ◽  
Fluid Dynamic ◽  
Labyrinth Seal ◽  
Surrogate Models ◽  
Robust Design Optimization ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Total Enthalpy ◽  
Multi Objective ◽  
Conflicting Objectives

This work presents a robust multi-objective optimization of a labyrinth seal used in power plants steam turbines. The conflicting objectives of this optimization are to minimize the mass flow and to minimize the total enthalpy increase in order to increase the performance and to reduce the temperature, which results in elevated component utilization. The focus should be the robustness aspect to be involved into the optimization. So that the final design is not only optimized for its deterministic values but also robust under its uncertainties. To achieve a robust and optimized design, surrogate models are trained and used to replace the computational fluid dynamic solver (CFD), so as to speed up the calculations. In contrast to most techniques used in literature, the robustness criteria are directly involved in the multi-objective optimization. This leads to a more robust Pareto front compared to a purely deterministic one. This method needs many design evaluations, which would be not effective, if a CFD solver were used.

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