A Comparison of Methods for Selecting Preferred Solutions in Multiobjective Decision Making

Zio, Enrico; Bazzo, Roberta

doi:10.2991/978-94-91216-77-0_2

Cited by 23 publications

(10 citation statements)

References 14 publications

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“…The use of clustering techniques is a common approach to help make the selection of solution from a large Pareto optimal set more manageable (Aguirre and Taboada, 2011;Zio and Bazzo, 2012;Chaudhari et al, 2013). Clustering aims to group objects with similar characteristics into distinct partitions, or clusters.…”

Section: Filtering Representative Solutions With Clusteringmentioning

confidence: 99%

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

et al. 2019

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Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem.

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Section: Filtering Representative Solutions With Clusteringmentioning

confidence: 99%

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

et al. 2019

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“…In a posteriori methods, a representative set of Pareto optimal solutions is first found and then the decision maker (DM) should choose one of the obtained points using higher-level information. The DM is expected to be an expert in the problem domain although several decision making support methods are developed to aid the DM in the selection of the preferred solutions 15 16 17 .…”

Section: Methodsmentioning

confidence: 99%

Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

Vafaee

2016

Sci Rep

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Biomarkers have gained immense scientific interest and clinical value in the practice of medicine. With unprecedented advances in high-throughput technologies, research interest in identifying novel and customized disease biomarkers for early detection, diagnosis, or drug responses is rapidly growing. Biomarkers can be identified in different levels of molecular biomarkers, networks biomarkers and dynamical network biomarkers (DNBs). The latter is a recently developed concept which relies on the idea that a cell is a complex system whose behavior is emerged from interplay of various molecules, and this network of molecules dynamically changes over time. A DNB can serve as an early-warning signal of disease progression, or as a leading network that drives the system into the disease state, and thus unravels mechanisms of disease initiation and progression. It is therefore of great importance to identify DNBs efficiently and reliably. In this work, the problem of DNB identification is defined as a multi-objective optimization problem, and a framework to identify DNBs out of time-course high-throughput data is proposed. Temporal gene expression data of a lung injury with carbonyl chloride inhalation exposure has been used as a case study, and the functional role of the discovered biomarker in the pathogenesis of lung injury has been thoroughly analyzed.

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“…A survey of a priori and post priori methods of supporting decision makers faced with a Pareto front of solutions to choose from is presented in [4], the authors use a multiobjective evolutionary algorithm to solve a multi-objective control problem, the output of which is a non-dominated front of potential solutions. Methods of decision support include self-organising maps and subtractive clustering, fuzzy scoring and data envelopment analysis (all post priori approaches) and a guided multi-objective genetic algorithm (a priori).…”

Section: Previous Workmentioning

confidence: 99%

Evaluating the Performance of an Evolutionary Tool for Exploring Solution Fronts

Urquhart

2018

Applications of Evolutionary Computation

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EvoFilter is an evolutionary algorithm based tool for searching through large non-dominated fronts in order to find a subset of solutions that are of interest to the user. EvoFilter is designed to take the output of existing Multi Objective Evolutionary Algorithms and act as a decision support tool for users. Currently EvoFilter is available for all to use on-line [1]. This paper evaluates the performance of EvoFilter by creating a large number of randomised filter specifications which are then applied using EvoFilter and a simple filter to a range of nondominated fronts created by a portfolio of Multi Objective Genetic Algorithms (MOGAs). The results show that EvoFilter is capable of finding sets of solutions that meet the users' requirements more closely than those found using the simple filter. EvoFilter increases performance on some objectives by including relevant solutions event if these solutions slightly lessen performance on other objectives. The filter discussed in this paper may be accessed at [1].

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A Comparison of Methods for Selecting Preferred Solutions in Multiobjective Decision Making

Cited by 23 publications

References 14 publications

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

Evaluating the Performance of an Evolutionary Tool for Exploring Solution Fronts

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