A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics

Salmasnia, Ali; Kazemzadeh, Reza Baradaran; Tabrizi, Mohammad Mohajer

doi:10.1016/j.neucom.2012.03.001

Cited by 29 publications

(13 citation statements)

References 34 publications

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“…Our method also incorporate missing data in a simple way but more sophisticated methods can be used. For example, one may wish to incorporate correlations among the multiple outcomes in ways similar to recent ideas proposed in the literature [25,21]. Our application was for rheumatic diseases but the method is general and can be used to broadly analyze other longitudinal studies as well.…”

Section: Discussionmentioning

confidence: 99%

Data-driven desirability function to measure patients’ disease progression in a longitudinal study

Chen

Wong

2015

Journal of Applied Statistics

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Multiple outcomes are increasingly used to assess chronic disease progression. We discuss and show how desirability functions can be used to assess a patient overall response to a treatment using multiple outcome measures and each of them may contribute unequally to the final assessment. Because judgments on disease progression and the relative contribution of each outcome can be subjective, we propose a data-driven approach to minimize the biases by using desirability functions with estimated shapes and weights based on a given gold standard. Our method provides each patient with a meaningful overall progression score that facilitates comparison and clinical interpretation. We also extend the methodology in a novel way to monitor patients’ disease progression when there are multiple time points and illustrate our method using a longitudinal data set from a randomized two-arm clinical trial for scleroderma patients.

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Section: Discussionmentioning

confidence: 99%

Data-driven desirability function to measure patients’ disease progression in a longitudinal study

Chen

Wong

2015

Journal of Applied Statistics

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“…There are several examples of using GA in multi-response optimization problems. For example, refer to Cheng et al [33], Pasandideh and Niaki [34], Noorossana et al [6], Salmasnia et al [35], and Ouyang et al [36]. However, it should be noted that GA is not the only alternative and the optimization algorithm may be selected by analyst based on his/her preferences and the problem conditions.…”

Section: Proposed Methodologymentioning

confidence: 99%

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

2017

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In some processes, quality of a product should be characterized by functional relationships between response variables and a signal factor. Hence the traditional methods cannot be used to find the optimum solution. In this paper, we propose a method which considers two different dispersion effects, i.e. in domain and between replicates variations in the functional responses. Besides, we propose an integral based measure to find the deviation from target function. A probabilistic method is applied to consider the correlation structure of functional responses. Three numerical examples and a real case from literature are studied to show the efficiency of the proposed method.

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“…A comparison of the results revealed that BBO outperformed the others. Salmasnia et al [18] presented a three-phased approach that uses principal component analysis (PCA), adaptive-network-based fuzzy inference systems (ANFIS), desirability function, and genetic algorithms (GAs) to simultaneously optimize multiple correlated responses where the relationships between responses and design variables are highly nonlinear.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Cost-based Procedure for Multi-response Parameter Design Problems Using GEP, Taguchi Quality Loss, and PSO: Case Study on Heat Sink Design

Hsu¹

2014

International Journal of Computational Intelligence Systems

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Most modern products/processes usually have several quality characteristics that must be optimized simultaneously; this is called a multi-response parameter design problem. To overcome shortcomings in the literature, including insufficient accuracy of second-order polynomials, subjective determination of relative weights and shape coefficients, and non-consideration of manufacturing or material costs, this paper proposes a cost-based procedure for resolving multi-response parameter design problems using gene expression programming (GEP), Taguchi quality loss, and particle swarm optimization (PSO). A case study with the aim of optimizing the design of a heat sink applied to a high-power MR16 LED lamp was used to demonstrate the proposed procedure. The experimental results indicated that the proposed procedure can provide highly robust settings for design parameters that can maximize the thermal performance and minimize the actual material cost of a heat sink. Furthermore, decisionmakers no longer need to subjectively determine the relative weight of each response. Therefore, the proposed approach can be considered to be feasible and effective; it has the potential to be a useful tool for resolving general multi-response parameter design problems in the real world.

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A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics

Cited by 29 publications

References 34 publications

Data-driven desirability function to measure patients’ disease progression in a longitudinal study

Data-driven desirability function to measure patients’ disease progression in a longitudinal study

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

Cost-based Procedure for Multi-response Parameter Design Problems Using GEP, Taguchi Quality Loss, and PSO: Case Study on Heat Sink Design

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