Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods

Cruz-Reyes, Laura; Fernández, Eduardo; Sánchez-Solís, J. Patricia; Coello, Carlos A. Coello; Gómez, Consuelo

doi:10.1016/j.swevo.2020.100652

Cited by 16 publications

(19 citation statements)

References 26 publications

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“…In the case of outranking relationship, indicators of dominance or preference are defined given some thresholds. This approach's main criticism is the difficulty to obtain the model parameters [6]; however, there are methods to solve it [17]. On the other hand, MAUT does not work when intransitivity exists between the preferential model [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Expressing a DM's preference could be a more difficult cognitive process. According to Cruz et al [6], the following characteristics are desirable for a preference incorporation method:…”

Section: Literature Reviewmentioning

confidence: 99%

“…Incorporating DM's preferences requires considering non-trivial aspects-defining the DM's preferences, determining the ROI and determining the relevance of a solution [5]. The preferences incorporation methods have used the following representation structures [6,7]-weights, ranking of solutions, ranking of objective functions, reference point, trade-offs between objective functions, desirability thresholds, outranking relations. This paper incorporates preferences using outranking relations.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the lack of research devoted to studying the convenience of using the outranking approach in the optimization process, this work proposes a further analysis to observe the performance of a novel strategy of incorporating outranking in a MOEA. Unlike Cruz et al [6], which requires representative solutions of two classes from the DM, this work proposes to incorporate two classes for internal use to guide the search process and establish greater differentiation between solutions, exerting selective pressure to find the ROI, but with the same cognitive load for the DM.…”

mentioning

confidence: 99%

“…In Cruz et al [6], the multicriteria ordinal classification requires the DM to separate solutions into two categories. In a preference incorporation method with this classifier, the human categorization is the stage with the lowest cognitive demand of the entire process.…”

mentioning

confidence: 99%

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A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

Castellanos-Alvarez

Cruz-Reyes

Fernández

et al. 2021

MCA

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Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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Section: Literature Reviewmentioning

confidence: 99%

“…Expressing a DM's preference could be a more difficult cognitive process. According to Cruz et al [6], the following characteristics are desirable for a preference incorporation method:…”

Section: Literature Reviewmentioning

confidence: 99%