scholarly journals Searching for the Pareto frontier in multi-objective protein design

2017 ◽  
Vol 9 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Vikas Nanda ◽  
Sandeep V. Belure ◽  
Ofer M. Shir
Keyword(s):  
Protein Design ◽  
Pareto Frontier ◽  
Multi Objective

scholarly journals A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

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.

scholarly journals Optimizing the 3D Distributed Climate inside Greenhouses Using Multi-Objective Optimization Algorithms and Computer Fluid Dynamics

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2873 ◽  
Author(s):  
Kangji Li ◽  
Wenping Xue ◽  
Hanping Mao ◽  
Xu Chen ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Field Experiments ◽  
Climate Model ◽  
Pareto Frontier ◽  
Environmental Parameters ◽  
Optimization Method ◽  
Crop Growth ◽  
Control Variables ◽  
Greenhouse Production ◽  
Multi Objective

As one of the major production facilities in agriculture, a greenhouse has many spatial distributed factors influencing crop growth and energy consumption, such as temperature field, air flow pattern, CO 2 concentration distribution, etc. By introducing a hybrid computational fluid dynamics–evolutionary algorithm (CFD-EA) method, this paper constructs a micro-climate model of greenhouse with main environmental parameters optimized. Considering environmental factors’ spatial influences together with energy usage simultaneously, the optimal solutions of control variables for crop growth are calculated. A commercial greenhouse located in east China is chosen for the method validation. Field experiments using temperature/velocity sensor matrix are carried out for CFD accuracy investigation. On this basis, the proposed optimization method is employed to search for the optimal control variables and parameters corresponding to the environmental Pareto frontier. By the proposed multi-objective scheme, we believe the method can provide set point basis for the design and regulation of large/medium-sized greenhouse production with high spatial resolution.

Effective Generation of Pareto Sets Using Genetic Programming

2001 ◽  
Author(s):  
John Eddy ◽  
Kemper Lewis
Keyword(s):  
Genetic Programming ◽  
Optimization Problem ◽  
Complete Solution ◽  
Pareto Frontier ◽  
Multi Objective ◽  
Pareto Sets ◽  
Effective Generation ◽  
Final Design ◽  
The Individual ◽  
Measure Of Performance

Abstract Many designers concede that there is typically more than one measure of performance for an artifact. Often, a large system is decomposed into smaller subsystems each having its own set of objectives, constraints, and parameters. The performance of the final design is a function of the performances of the individual subsystems. It then becomes necessary to consider the tradeoffs that occur in a multi-objective design problem. The complete solution to a multi-objective optimization problem is the entire set of non-dominated configurations commonly referred to as the Pareto set. Common methods of generating points along a Pareto frontier involve repeated conversion of multi-objective problems into single objective problems using weights. These methods have been shown to perform poorly when attempting to populate a Pareto frontier. This work presents an efficient means of generating a thorough spread of points along a Pareto frontier using genetic programming.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

Optimum Design of a Compliant Uniaxial Accelerometer

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Simon Desrochers ◽  
Damiano Pasini ◽  
Jorge Angeles
Keyword(s):  
Stress Concentration ◽  
Optimum Design ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Pareto Frontier ◽  
Cad Model ◽  
Multi Objective Optimization ◽  
Design Objective ◽  
Multi Objective ◽  
Pareto Plot

This work focuses on the multi-objective optimization of a compliant-mechanism accelerometer. The design objective is to maximize the sensitivity of the accelerometer in its sensing direction, while minimizing its sensitivity in all other directions. In addition, this work proposes a novel compliant hinge intended to reduce the stress concentration in compliant mechanisms. The paper starts with a brief description of the new compliant hinge, the Lamé-shaped hinge, followed by the formulation of the aposteriori multi-objective optimization of the compliant accelerometer. By using the normalized constrained method, an even distribution of the Pareto frontier is found. The paper also provides several optimum solutions on a Pareto plot, as well as the CAD model of the selected solution.

Various Criteria in Optimization of a Vapor Compression Refrigeration System

2010 ◽  
Author(s):  
Mostafa Nejatolahi ◽  
Hoseyn Sayaadi
Keyword(s):  
Pareto Frontier ◽  
Vapor Compression ◽  
Economic Criterion ◽  
Multi Objective ◽  
Total Product ◽  
Energy And Exergy ◽  
Vapor Compression Refrigeration ◽  
Exergy Analyses ◽  
Single Objective ◽  
Selection Of

A cooling tower assisted vapor compression refrigeration machine has been considered for optimization with multiple criteria. Two objective functions including the total exergy destruction of the system (as a thermodynamic criterion) and the total product cost of the system (as an economic criterion), have been considered simultaneously. A thermodynamic model based on the energy and exergy analyses and an economic model according to the Total Revenue Requirement (TRR) method have been presented. Three optimized systems including a single-objective thermodynamic optimized, a single-objective economic optimized and a multi-objective optimized are obtained. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from the Pareto frontier has been presented. The exergetic and economic results obtained for three optimized systems have been compared and discussed. The results have shown that the multi-objective design more acceptably satisfies generalized engineering criteria than other two single-objective optimized designs.

Pareto Frontier of a Dynamic Principal–Agent Model with Discrete Actions: An Evolutionary Multi-Objective Approach

2011 ◽  
Vol 40 (4) ◽  
pp. 415-443
Author(s):  
Itza T. Q. Curiel ◽  
Sonia B. Di Giannatale ◽  
Juan A. Herrera ◽  
Katya Rodríguez
Keyword(s):  
Pareto Frontier ◽  
Principal Agent ◽  
Agent Model ◽  
Multi Objective ◽  
Principal Agent Model

Multi-Objective Optimization of Axial Crush Performance of Square Metal–Composite Hybrid Tubes

2013 ◽  
Author(s):  
Roozbeh Kalhor ◽  
Hossein Akbarshahi ◽  
Scott W. Case
Keyword(s):  
Impact Load ◽  
Pareto Frontier ◽  
Back Propagation ◽  
Design Parameters ◽  
Metal Composite ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Axial Crush ◽  
Composite Layers ◽  
Test Sets

This article deals with the multi objective optimization of square hybrid tubes (metal-composite) under axial impact load. Maximum crushing load and absorbed energy are objective functions and fiber orientation angles of the composite layers are chosen as design parameters while the maximum crush load is limited. Back-propagation artificial neural networks (ANNs) are utilized to construct the mapping between the variables and the objectives. Non-dominated sorting Genetic algorithm–II (NSGAII) is applied to obtain the optimal solutions and the finite element commercial software LS-DYNA is used to generate the training and test sets for the ANNs. Optimum results are presented as a Pareto frontier.

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