Smart Pareto filter: obtaining a minimal representation of multiobjective design space

Mattson, Christopher A.; Mullur, Anoop A.; Messac, Achille

doi:10.1080/0305215042000274942

Cited by 133 publications

(91 citation statements)

References 17 publications

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“…Indeed, 1300-1600 globally non-dominated solutions were found (only three are shown in the table), which is too high a number to study trade-offs. The number can however be reduced to a few representatives of their respective neighbourhood by applying a filtering method, like that of, for example, Mattson et al (2004).…”

Section: Case Study -Stepmentioning

confidence: 99%

Design of evacuation plans for densely urbanised city centres

Coutinho-Rodrigues

Sousa

Natividade-Jesus

2016

Proceedings of the Institution of Civil Engineers - Municipal E

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The high population density and tightly packed nature of some city centres make emergency planning for these urban spaces especially important, given the potential for human loss in case of disaster. Historic and recent events have made emergency service planners particularly conscious of the need for preparing evacuation plans in advance. This paper discusses a methodological approach for assisting decision-makers in designing urban evacuation plans. The approach aims at quickly and safely moving the population away from the danger zone into shelters. The plans include determining the number and location of rescue facilities, as well as the paths that people should take from their building to their assigned shelter in case of an occurrence requiring evacuation. The approach is thus of the location-allocation-routing type, through the existing streets network, and takes into account the trade-offs among different aspects of evacuation actions that inevitably come up during the planning stage. All the steps of the procedure are discussed and systematised, along with computational and practical implementation issues, in the context of a case study -the design of evacuation plans for the historical centre of an old European city.

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Section: Case Study -Stepmentioning

confidence: 99%

Design of evacuation plans for densely urbanised city centres

Coutinho-Rodrigues

Sousa

Natividade-Jesus

2016

Proceedings of the Institution of Civil Engineers - Municipal E

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“…Probably, one of the most popular partitional methods is the k-means clustering algorithm. The k-means clustering algorithm is well known for its efficiency in clustering data sets [11]. The grouping is done by calculating the centroid for each cluster, and assigning each observation to the group with the closest centroid.…”

Section: Cluster Analysismentioning

confidence: 99%

Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary

Chaudhari¹,

Dharaskar²,

Thakare³

2010

IJACSA

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Abstract-Problems with multiple objectives can be solved by using Pareto optimization techniques in evolutionary multiobjective optimization algorithms. Many applications involve multiple objective functions and the Pareto front may contain a very large number of points. Selecting a solution from such a large set is potentially intractable for a decision maker. Previous approaches to this problem aimed to find a representative subset of the solution set. Clustering techniques can be used to organize and classify the solutions. Implementation of this methodology for various applications and in a decision support system is also discussed.

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“…These individual groupings correspond to different tradeoffs between the two objectives and, in our case, also consist of different numbers of clusters. Several researchers have already investigated the identification of promising solutions from Pareto front approximations recently [22,23]. For choosing the most interesting solutions from the Pareto front, we apply Tibshirani et al's Gap statistic [24], a statistical method to determine the number of clusters in a data set.…”

Section: Selecting the Best Solution From Pareto-frontmentioning

confidence: 99%