Information to the Eye of the Beholder: Data Visualization for Many-Objective Optimization

Meneghini, Ivan Reinaldo; Koochaksaraei, Roozbeh Haghnazar; Guimarães, Frederico Gadelha; Gaspar‐Cunha, A.

doi:10.1109/cec.2018.8477889

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…The set of 210 solutions obtained with the GPD is illustrated through the CAPviz [30] With those alternatives, subsets of sizes 5, 7 and 10 were evaluated, symbolizing the number of solutions presented to the DM per time (or iteration). The key idea, as aforementioned explained, was to use just some solutions to learn the In a new iteration, more 5 were presented to the DM that made the pairwise comparisons and these preferences were aggregated with the previous subset.…”

Section: Resultsmentioning

confidence: 99%

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Learning Pairwise Comparisons with Machine Learning for Large-Scale Multi-Criteria Decision Making Problems

Alves¹,

Meneghini²,

Guimarães³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Decision making is a complex task and requires a lot of cognitive effort from the decision maker. Multi-criteria methods, especially those based on pairwise comparisons, such as the Analytic Hierarchic Process (AHP), are not viable for large-scale decision-making problems. For this reason, the aim of this paper is to learn the preferences of the decision-maker using machine learning techniques in order to reduce the number of queries that are necessary in decision problems. We used a recently published parameterized generator of scalable and customizable benchmark problems for many-objective problems as a large-scale data generator. The proposed methodology is an iterative method in which a small subset of solutions are presented to the decision-maker to obtain pairwise judgments. This information is fed to an algorithm that learns the preferences for the remaining pairs in the decision matrix. The Gradient Boosting Regressor was applied in a problem with 5 criteria and 210 solutions. Subsets of 5, 7 and 10 solutions were used in each iteration. The metrics MSE, RMSE, MAPE and R2 were calculated. After the 8th iteration the ranking similarity stabilized, as measured by the tau distance. As the main advantage of the proposed approach is that it was necessary only 8 iterations presenting 5 solutions per time to learn the preferences and get an accurate final ranking.

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Section: Resultsmentioning

confidence: 99%

“…The optimization part was implemented in Matlab by using the PlatEMO. For vizualization, CAPviz tool [30] was used. CAPviz integrates Chord diagram and Angular mapping and Parallel coordinates into a single two-dimensional circular chart.…”

Section: • Step 9: Calculate Tau Between Rankingsmentioning

confidence: 99%

Learning Pairwise Comparisons with Machine Learning for Large-Scale Multi-Criteria Decision Making Problems

Alves¹,

Meneghini²,

Guimarães³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

Self Cite

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“…Lastly, Figure 6e shows the constraint defined by Eq. ( 22) in a problem with eight objectives using the CAP-vis tool [50,51]. In this example, the Pareto front consists only of points near the axis of the second objective in terms of angular distance.…”

Section: Equality and Inequality Constraintsmentioning

confidence: 99%

Scalable and customizable benchmark problems for many-objective optimization

Meneghini

Alves

Gaspar‐Cunha

et al. 2020

Applied Soft Computing

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Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known.

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“…In the past decades, the complexity and dependency of the entities have been increased dramatically in the produced data sets throughout of science and industry. Therefore, the demand of meaningful, readable, reliable illustration and analyzing has been increasing simultaneously [13][14][15][16][17]. In the other word, this platform is capable of providing this kind of analyzing in Robotic, Society, Archiving, Publication, Economic fields, and area [18,19].…”

Section: Introductionmentioning

confidence: 99%

Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis

et al. 2021

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Data and information visualization have drawn an increasingly wide range of interest from several academic fields and industries. Concurrently, exploring a huge set of data to support feasible decisions needs an organized method of Multi-Criteria Decision Making (MCDM). The dramatic increasing of data producing during the past decade makes visualization necessary as a presentation layer on the top of MCDM process. This study aims to propose an integrated strategy to rank the alternatives in the dataset, by combining data, MCDM methods, and visualization layers. In fact, the well designed combination of Information Visualization and MCDM provides a more user-friendly approach than the traditional methods. We investigate a case study in bibliometric analyses, which have become an important dimension and tool for evaluating the impact and performance of researchers, departments, and universities. Hence, finding the best and most reliable papers, authors, and publishers considering diverse criteria is one of the important challenges in science world. Therefore, this text is presenting a new strategy on the bibliometric dataset as a case study and it demonstrates that this strategy can be more meaningful for the end users than the current tools. Finally, the presented simulations illustrate the performance and utilization of this combination. In other words, the researchers of this study could design and implement a tool that overcomes the biggest challenges of data analyzing and ranking via a combination of MCDM and visualization methodologies that can provide a tremendous amount of insight and information from a massive dataset in an efficient way.

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Information to the Eye of the Beholder: Data Visualization for Many-Objective Optimization

Cited by 9 publications

References 27 publications

Learning Pairwise Comparisons with Machine Learning for Large-Scale Multi-Criteria Decision Making Problems

Learning Pairwise Comparisons with Machine Learning for Large-Scale Multi-Criteria Decision Making Problems

Scalable and customizable benchmark problems for many-objective optimization

Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis

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