scholarly journals Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

2018 ◽  
Author(s):  
Ester Forte ◽  
Erik von Harbou ◽  
Jakob Burger ◽  
Michael Bortz ◽  
Norbert Asprion
Keyword(s):  
Experimental Design ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Of Experiments ◽  
Pilot Plant ◽  
Pareto Frontier ◽  
Trade Offs ◽  
Interesting Part ◽  
Conflicting Objectives ◽  
Statistical Approaches

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

scholarly journals Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

2021 ◽  
Author(s):  
Esther Forte ◽  
Erik von Harbou ◽  
Jakob Burger ◽  
Norbert Asprion ◽  
Michael Bortz
Keyword(s):  
Experimental Design ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Of Experiments ◽  
Pilot Plant ◽  
Pareto Frontier ◽  
Trade Offs ◽  
Interesting Part ◽  
Conflicting Objectives ◽  
Statistical Approaches

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

Entropy-based Optimization for Heat Transfer Enhancement in Tubes with Helical Fins

2021 ◽  
Author(s):  
Bharath Pidaparthi ◽  
Peiwen Li ◽  
Samy Missoum
Keyword(s):  
Heat Transfer ◽  
Optimal Design ◽  
Multiobjective Optimization ◽  
Heat Transfer Enhancement ◽  
Point Of View ◽  
Design Solution ◽  
Total Entropy ◽  
Transfer Enhancement ◽  
Pressure Drops ◽  
Conflicting Objectives

Abstract In this work, a tube with internal helical fins is analyzed and optimized from an entropy generation point of view. Helical fins, in addition to providing heat transfer enhancements, have the potential to level the temperature of the tube under non-uniform circumferential heating. The geometric parameters of helical fins are optimized under two different entropy-based formulations. Specifically, this work focuses on comparing the optimal design solution obtained through the minimization of total entropy and through the multiobjective optimization of the thermal and viscous entropy contributions when considered as two separate objectives. The latter quantities being associated with heat transfer and pressure drops, it is shown that, from a design optimization point of view, it is important to separate both entropies which are conflicting objectives.

scholarly journals Support Vector Machine Applied to the Optimal Design of Composite Wing Panels

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 328
Author(s):  
Rogério Rodrigues dos Santos ◽  
Tulio Gomes de Paula Machado ◽  
Saullo Giovani Pereira Castro
Keyword(s):  
Support Vector Machine ◽  
Optimal Design ◽  
Learning Strategy ◽  
Pareto Frontier ◽  
Laminated Composite ◽  
Deterministic Algorithm ◽  
Simultaneous Optimization ◽  
Support Vector ◽  
Limited Information ◽  
Conflicting Objectives

One of the core technologies in lightweight structures is the optimal design of laminated composite stiffened panels. The increasing tailoring potential of new materials added to the simultaneous optimization of various design regions, leading to design spaces that are vast and non-convex. In order to find an optimal design using limited information, this paper proposes a workflow consisting of design of experiments, metamodeling and optimization phases. A machine learning strategy based on support vector machine (SVM) is used for data classification and interpolation. The combination of mass minimization and buckling evaluation under combined load is handled by a multi-objective formulation. The choice of a deterministic algorithm for the optimization cycle accelerates the convergence towards an optimal design. The analysis of the Pareto frontier illustrates the compromise between conflicting objectives. As a result, a balance is found between the exploration of new design regions and the optimal design refinement. Numerical experiments evaluating the design of a representative upper skin wing panel are used to show the viability of the proposed methodology.

scholarly journals Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
P. Sabarinath ◽  
M. R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Production Costs ◽  
Machining Parameters ◽  
Conflicting Objectives

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

scholarly journals Optimal Design of Laboratory and Pilot-Plant Experiments Using Multiobjective Optimization

2017 ◽  
Vol 89 (5) ◽  
pp. 645-654 ◽  
Author(s):  
Esther Forte ◽  
Erik von Harbou ◽  
Jakob Burger ◽  
Norbert Asprion ◽  
Michael Bortz
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Pilot Plant

scholarly journals Maximal Power Tests for Detecting Defects in Meiotic Recombination

Genetics ◽  
2002 ◽  
Vol 161 (3) ◽  
pp. 1333-1337
Author(s):  
Thomas I Milac ◽  
Frederick R Adler ◽  
Gerald R Smith
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Meiotic Recombination ◽  
Region Of Interest ◽  
Genetic Distances ◽  
Genomic Region ◽  
Maximal Power ◽  
Optimal Design Of Experiments ◽  
Crossover Interference ◽  
Single Interval

Abstract We have determined the marker separations (genetic distances) that maximize the probability, or power, of detecting meiotic recombination deficiency when only a limited number of meiotic progeny can be assayed. We find that the optimal marker separation is as large as 30–100 cM in many cases. Provided the appropriate marker separation is used, small reductions in recombination potential (as little as 50%) can be detected by assaying a single interval in as few as 100 progeny. If recombination is uniformly altered across the genomic region of interest, the same sensitivity can be obtained by assaying multiple independent intervals in correspondingly fewer progeny. A reduction or abolition of crossover interference, with or without a reduction of recombination proficiency, can be detected with similar sensitivity. We present a set of graphs that display the optimal marker separation and the number of meiotic progeny that must be assayed to detect a given recombination deficiency in the presence of various levels of crossover interference. These results will aid the optimal design of experiments to detect meiotic recombination deficiency in any organism.

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

Multi-Objective Optimal Design of Passive Suspension System With Inerter Damper

2018 ◽  
Author(s):  
Xiaotian Xu ◽  
Yousef Sardahi ◽  
Chenyu Zheng
Keyword(s):  
Optimal Design ◽  
Suspension System ◽  
Design Parameters ◽  
Head Acceleration ◽  
Crest Factor ◽  
Nsga Ii ◽  
Passive Suspension ◽  
Trade Offs ◽  
Passive Suspension System ◽  
Unsprung Mass

This paper presents a many-objective optimal design of a four-degree-of-freedom passive suspension system with an inerter device. In the optimization process, four objectives are considered: passenger’s head acceleration (HA), crest factor (CF), suspension deflection (SD), and tire deflection (TD). The former two objectives are important for the health and comfort of the driver and the latter two quantify the suspension system performance. The spring ks and damping cs constants between the sprung mass and unsprung mass, the inertance coefficient B, and the tire spring constant ky are considered as design parameters. The non-dominated sorting genetic algorithm (NSGA-II) is used to solve this optimization problem. The results show that there are many optimal trade-offs among the design objectives that could be applicable to suspension design in the industry.

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