scholarly journals Some applications of a novel desirability function in simultaneous optimization of multiple responses

2021 ◽  
Vol 49 (3) ◽  
pp. 534-548
Author(s):  
Velibor Marinković
Keyword(s):  
Comparative Analysis ◽  
Desirability Function ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
The Other ◽  
Simultaneous Optimization ◽  
Desirability Functions ◽  
Gradient Based ◽  
Response Optimization ◽  
Optimization Methodology

In the framework of multi-response optimization techniques, the optimization methodology based on the desirability function is one of the most popular and most frequently used methodologies by researchers and practitioners in engineering, chemistry, technology and many other fields of science and technique. Numerous desirability functions have been introduced to improve the performance of this optimization methodology. Recently, a novel desirability function for multi-response optimization is proposed, which is smooth, nonlinear, and differentiable, and thus more suitable for applying some of the more efficient gradient-based optimization methods. This paper evaluates the performance of the proposed method through six real examples. After a comparative analysis of the results, it is shown that the proposed method in a certain measure outperforms the other competitive optimization methods.

scholarly journals A novel desirability function for multi-response optimization and its application in chemical engineering

2020 ◽  
Vol 26 (3) ◽  
pp. 309-319
Author(s):  
Velibor Marinkovic
Keyword(s):  
Chemical Engineering ◽  
Desirability Function ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
The Other ◽  
Individual Response ◽  
Differentiable Functions ◽  
Modern Engineering ◽  
Gradient Based ◽  
Response Optimization

In many aspects of modern engineering (processes, products, and systems)InIn many aspects of modern engineering (processes, products, and systems) there is a need to optimize multiple responses simultaneously, rather than optimizing one response at a time. The optimization of each individual response may generate as many different results as the responses, which is considered in the study. Then, it may be impossible to decide whether one solution is better than the other. On the other hand, some improvement in one response can significantly degrade at least one or more responses. There are a number of multi-response optimization techniques available. Among these multi-response optimization techniques, the desirability-based approaches take a prominent place because they are less sophisticated, easy to understand and implement, and more flexible with respect to other existing approaches, due to which they are very popular among researchers and practitioners. There are many different formulations of the desirability function. Unfortunately, most desirability functions known in the literature are piecewise, non-differentiable functions. In this paper, a novel desirability function is proposed, which is continuous and differentiable in its domain. This function is more suitable for applying some of the efficient gradient-based optimization methods. The efficiency and accuracy of the proposed method were analyzed on two chemical processes that were studied extensively in the literature.

Estimation of Thermal Boundary Conditions by Gradient Based and Genetic Algorythms

2012 ◽  
Vol 729 ◽  
pp. 144-149 ◽  
Author(s):  
Imre Felde
Keyword(s):  
Heat Transfer ◽  
Boundary Conditions ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Thermal Boundary Conditions ◽  
Treatment Processes ◽  
Gradient Based ◽  
Type Boundary

The prediction of third type boundary conditions occurring during heat treatment processes is an essential requirement for characterization of heat transfer phenomena. In this work, the performance of four optimization techniques is studied. These models are the Conjugate Gradient Method, the Levenberg-Marquardt Method, the Simplex method and the NSGA II algorithm. The models are used to estimate the heat transfer coefficient during transient heat transfer. The performance of the optimization methods is demonstrated using numerical techniques.

Experimental investigation of nanofluids in minimum quantity lubrication during turning of EN-24 steel

2019 ◽  
Vol 234 (5) ◽  
pp. 712-729
Author(s):  
Archana Thakur ◽  
Alakesh Manna ◽  
Sushant Samir
Keyword(s):  
Comparative Analysis ◽  
Desirability Function ◽  
Cutting Temperature ◽  
Minimum Quantity Lubrication ◽  
Hybrid Nanofluid ◽  
Machining Performance ◽  
Optimization Of Parameters ◽  
Minimum Quantity ◽  
Response Optimization ◽  
Hybrid Nanofluids

The present work evaluates the performance of different machining environments such as dry, wet, minimum quantity lubrication, Al2O3 nanofluids based minimum quantity lubrication, CuO nanofluids based minimum quantity lubrication and Al–CuO hybrid nanofluids based minimum quantity lubrication on machining performance characteristics during turning of EN-24. The nanofluids and hybrid nanofluids were prepared by adding the Al2O3, CuO and Al2O3/CuO to the soluble oil with different weight percentages (0.5 wt.%, 1 wt.%, 1.5 wt.%). The thermal and tribological properties of hybrid nanofluid and nanofluids were analyzed. The comparative analysis of different turning environments has been done. From comparative analysis it is clearly observed that the nanofluids and hybrid nanofluid shows better performance during turning of EN-24 steel. So there is a need for optimization of parameters during turning of EN-24 under Al2O3 nanofluids based minimum quantity lubrication, CuO nanofluids based minimum quantity lubrication and Al–CuO hybrid nanofluids based minimum quantity lubrication. The optimization of parameters has been done by response surface methodology. The significance of developed model was identified from analysis of variance. Multi-response optimization was done using desirability function approach. To verify the accuracy of developed models, confirmatory experiments were performed. The experimental results reveal that Al–CuO hybrid nanofluids based minimum quantity lubrication significantly improves surface quality, reduces cutting temperature and cutting forces.

Metamodel-Based Optimum Design Examples of Structures

2014 ◽  
pp. 667-676
Author(s):  
Kwon-Hee Lee ◽  
Ji-In Heo
Keyword(s):  
Weight Reduction ◽  
Performance Enhancement ◽  
Fuel Efficiency ◽  
Optimization Methods ◽  
Optimization Method ◽  
The Other ◽  
True Response ◽  
Design Variables ◽  
Weight One ◽  
Gradient Based

In order to achieve greater fuel efficiency and energy conservation, the reduction of weight and enhancement of the performance of structures has been sought. In general, there are two approaches to reducing structural weight. One of which is to use materials that are lighter than steel and the other is to redesign the structure. However, conventional structural optimization methods using gradient-based algorithm directly have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome these difficulties a metamodel-based optimization method is introduced in order to replace the true response by an approximate one. This research presents four case studies of structural design using a metamodel-based approximation model for weight reduction or performance enhancement.

Simultaneous optimization of output parameters in HPC drilling using multi response optimization technique with desirability function approach

2020 ◽  
Vol 17 (3) ◽  
pp. 437-444
Author(s):  
Hanmant Virbhadra Shete ◽  
Madhav S. Sohani
Keyword(s):  
Mathematical Models ◽  
Response Surface ◽  
Desirability Function ◽  
Optimization Technique ◽  
Simultaneous Optimization ◽  
Drilling Process ◽  
Content Type ◽  
Input Parameters ◽  
Response Optimization ◽  
Desirability Function Approach

Purpose This paper aims to examine an investigation of high-pressure coolant (HPC) drilling process with regard to experimental models of output parameters, effect of input parameters on output parameters and simultaneous optimization of the output parameters. Design/methodology/approach Experimental plan was designed using response surface method and experiments were conducted on HPC drilling set up. Measurements for output parameters were carried out and mathematical models were obtained. Multi response optimization using a composite desirability function approach was used to obtain optimum values of input parameters for simultaneous optimization of output parameters. Findings Optimal value of input parameters for optimization of HPC drilling process were obtained as; coolant pressure: 21 bar, spindle speed: 3,970 rpm, feed rate: 0.084 mm/rev and peck depth: 5.50 mm. The composite desirability obtained is 0.9412, which indicates that the performance of HPC drilling process was significantly optimized. Developed mathematical models of the output parameters accurately represent the entire design space under investigation. Originality/value This is the first study that involves variation of higher coolant pressure and investigation of HPC drilling process using response surface methodology and multi response optimization technique with desirability function.

A novel simulation-annealing enabled ranking and scaling statistical simulation constrained optimization algorithm for Internet-of-things (IoTs)

2020 ◽  
Vol 9 (4) ◽  
pp. 675-693 ◽  
Author(s):  
Adarsh Kumar ◽  
Saurabh Jain ◽  
Divakar Yadav
Keyword(s):  
Comparative Analysis ◽  
Tabu Search ◽  
Critical Infrastructure ◽  
Simulation Optimization ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Evolutionary Strategies ◽  
Small Scale ◽  
Content Type ◽  
Simplex Search

PurposeSimulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.Design/methodology/approachIn this work, a comparative analysis of heuristic simulation optimization methods (genertic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed. Further, a novel simulation annealing-based heuristic approach is proposed for critical infrastructure.FindingsA small scale network of 50–100 nodes shows that genetic simulation optimization with multi-criteria and multi-dimensional features performs better as compared to other simulation optimization approaches. Further, a minimum of 3.4 percent and maximum of 16.2 percent improvement is observed in faster route identification for small scale Internet-of-things (IoT) networks with simulation optimization constraints integrated model as compared to the traditional method.Originality/valueIn this work, simulation optimization techniques are applied for identifying optimized Quality of service (QoS) parameters for critical infrastructure which in turn helps in improving the network performance. In order to identify optimized parameters, Tabu search and ant-inspired heuristic optimization techniques are applied over QoS parameters. These optimized values are compared with every monitoring sensor point in the network. This comparative analysis helps in identifying underperforming and outperforming monitoring points. Further, QoS of these points can be improved by identifying their local optimum values which in turn increases the performance of overall network. In continuation, a simulation model of bus transport is taken for analysis. Bus transport system is a critical infrastructure for Dehradun. In this work, feasibility of electric recharging units alongside roads under different traffic conditions is checked using simulation. The simulation study is performed over five bus routes in a small scale IoT network.

Numerical Methods for the Design of Meso-Structures: A Comparative Review

2015 ◽  
Author(s):  
Marcus Yoder ◽  
Zachary Satterfield ◽  
Mohammad Fazelpour ◽  
Joshua D. Summers ◽  
Georges Fadel
Keyword(s):  
Topology Optimization ◽  
Shape Optimization ◽  
Material Properties ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Future Research ◽  
Structured Materials ◽  
3 Dimensional ◽  
Comparative Review ◽  
Gradient Based

Over the past decade, there has been an increase in the intentional design of meso-structured materials that are optimized to target desired material properties. This paper reviews and critically compares common numerical methodologies and optimization techniques used to design these meso-structures by analyzing the methods themselves and published applications and results. Most of the reviewed research targets mechanical material properties, including effective stiffness and crushing energy absorption. The numerical methodologies reviewed include topology and size/shape optimization methods such as homogenization, Solid Isotropic Material with Penalization, and level sets. The optimization techniques reviewed include genetic algorithms (GAs), particle swarm optimization (PSO), gradient based, and exhaustive search methods. The research reviewed shows notable patterns. The literature reveals a push to apply topology optimization in an ever-growing number of 3-dimensional applications. Additionally, researchers are beginning to apply topology optimization and size/shape optimization to multiphysics problems. The research also shows notable gaps. Although PSOs are comparable evolutionary algorithms to GAs, the use of GAs dominates over PSOs. These patterns and gaps, along with others, are discussed in terms of possible future research in the design of meso-structured materials.

Solution of Correlated Multi-Response Optimization Problem

2010 ◽  
Vol 1 (4) ◽  
pp. 22-46 ◽  
Author(s):  
Saurav Datta ◽  
Goutam Nandi ◽  
Asish Bandyopadhyay ◽  
Pradip Kumar Pal
Keyword(s):  
Objective Function ◽  
Optimization Problem ◽  
Desirability Function ◽  
Optimization Technique ◽  
Principal Component ◽  
Optimization Techniques ◽  
Individual Response ◽  
Bead Geometry ◽  
Taguchi Optimization ◽  
Response Optimization

This paper highlights an integrated approach to solve the correlated multi-response optimization problem through a case study in submerged arc welding (SAW). The proposed approach has been presented to overcome different limitations and drawbacks of existing optimization techniques available in literature. Traditional Taguchi optimization technique is based under the assumption that quality responses are independent to each other; however, this assumption may not always be valid. A common trend in the solution of a multi-objective optimization problem is to convert these multi-objectives into an equivalent single objective function. While deriving this equivalent objective function, different weightage are assigned to different responses according to their relative priority. In this regard, it seems that no specific guideline is available for assigning individual response weighs. To avoid this, Principal Component Analysis (PCA) has been adopted to eliminate correlation among individual desirability values and to calculate uncorrelated quality indices that have been aggregated to calculate overall grey relational grade. This study combines PCA, Desirability Function (DF) approach, and grey relation theory to the entropy measurement technique. Finally, the Taguchi method has been used to derive optimal process environment capable of producing desired weld quality related to bead geometry.

An Efficient Multiple Description Coding for Multi-View Video Based on the Correlation of Spatial Polyphase Transformed Subsequences

2019 ◽  
Vol 63 (5) ◽  
pp. 50401-1-50401-7 ◽  
Author(s):  
Jing Chen ◽  
Jie Liao ◽  
Huanqiang Zeng ◽  
Canhui Cai ◽  
Kai-Kuang Ma
Keyword(s):  
Video Transmission ◽  
Video Sequence ◽  
Three Dimensional ◽  
The Other ◽  
Multiple Description Coding ◽  
Multiple Description ◽  
Prediction Mode ◽  
Gradient Based ◽  
Directional Interpolation ◽  
Description Coding

Abstract For a robust three-dimensional video transmission through error prone channels, an efficient multiple description coding for multi-view video based on the correlation of spatial polyphase transformed subsequences (CSPT_MDC_MVC) is proposed in this article. The input multi-view video sequence is first separated into four subsequences by spatial polyphase transform and then grouped into two descriptions. With the correlation of macroblocks in corresponding subsequence positions, these subsequences should not be coded in completely the same way. In each description, one subsequence is directly coded by the Joint Multi-view Video Coding (JMVC) encoder and the other subsequence is classified into four sets. According to the classification, the indirectly coding subsequence selectively employed the prediction mode and the prediction vector of the counter directly coding subsequence, which reduces the bitrate consumption and the coding complexity of multiple description coding for multi-view video. On the decoder side, the gradient-based directional interpolation is employed to improve the side reconstructed quality. The effectiveness and robustness of the proposed algorithm is verified by experiments in the JMVC coding platform.

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