Solving group multi-objective optimization problems by optimizing consensus through multi-criteria ordinal classification

Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

“…Other interesting avenues of future research are the following: i) the use of the proposed approaches to handle preferences combined with state-of the art many-objective evolutionary algorithms such as [11] and [40]; and ii) the combination of our results with the proposals of Fernández et al [24] and [2] in the context of group evolutionary multi-objective optimization.…”

“…The simulation of a DM uses random weights w and vetoes v for the value system DM = (w, v, , ) and the fixed values  = [0.51, 0.75] and  = [0.51, 0.67] for the majority and credibility thresholds, respectively. 2 The method that generates w = {w1, w2, …, wm} utilizes the strategy proposed by [8]. First, it generates a vector d of m -1 non-interval random numbers in the range (0, 1), following a uniform distribution.…”

“…In this sense, the aggregation of diverse parameter settings as interval numbers could be a reasonable model of collective preferences that could be used to identify solutions that are close to the so-called "social RoI." From a different perspective, in [2] and [24], consensus search in group evolutionary multi-objective optimization has been addressed by using the interval outranking approach. In such papers, consensus is identified through the optimization of a measure of group satisfaction/dissatisfaction.…”

An Interactive framework under conditions of uncertainty for multi-objective optimization

“…Decision-making or selection of alternative problems is complex process involving multiple criteria, for which it is necessary to use tools that allow discerning between them to obtain a solution that best satisfies the combination of possible alternatives (Gómez O. and Cabrera O., 2008). Furthermore, the problems generally include multiple objectives, which conflict with each other, making this process more complex and thus generating the need for a tool or method that allows these multiple criteria to be compared against the range of possible alternatives (Balderas F. et al, 2022).…”

“…When MS-DOSS generates the best final solutions, these make up an extensive set of solutions, so to help make the best decision, MS-DOSS sends the results to the module where the found solutions will be ordered. It should be noted, as mentioned before, that the data includes uncertainty; Therefore, the TOPSIS algorithm (Balderas F., 2022) was modified to handle the uncertainty, generating the ITOPSIS algorithm. https://mol2net-07.sciforum.net/ ITOPSIS (Interval Technique of Order Preference Similarity to the Ideal Solution) is a method for multicriteria decision analysis developed by Yoon in 1981.…”