Evolutionary multi-objective optimization for inferring outranking model’s parameters under scarce reference information and effects of reinforced preference

Abstract

Abstract. Methods based on fuzzy outranking relations constitute one of the main approaches to multiple criteria decision problems. The use of ELECTRE methods require the elicitation of a large number of parameters (weights and different thresholds); but direct eliciting is often a demanding task for the decision-maker (DM). For handling intensity-ofpreference effects on concordance levels, a generalized concordance model was proposed by Roy and Slowinski which is more complex than previous outranking models. In this paper, an evolutionary multi-objective-based indirect elicitation of the complete ELECTRE III model-parameter set is proposed. The evolutionary multi-objective inference method is successfully extended to inferring reinforced-preference model parameters. Wide experimental evidence is provided to support the proposal, which performs well even working on small size reference sets.

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

“…In multi-objective optimization, the DM 'learns' trade-offs while he/she finds and judges new Pareto solutions; thus his/her aprioristic preferences could be modified. Once the best compromise and others non-strictly outranked solutions have been obtained and evaluated by the DM, the model's parameter setting may be updated, perhaps using PDA as proposed in [63]. If the parameter values were modified, with an additional NO-ACO run the final best compromise should be reached.…”

“…Those judgments may be obtained from decisions made for a limited set of fictitious portfolios, or decisions taken for a subset of the portfolios under consideration for which the DM can easily make a judgment. In the framework of outranking methods, PDA has been recently approached in [62,63].…”

“…This can be done by an interaction between the DM and a Decision Analyst (DA), utilizing, if necessary, indirect elicitation methods to support this task [61,62,63]. The DM should assess the parameters included in:…”

Preference Incorporation into Evolutionary Multiobjective Optimization using Preference Information Implicit in a Set of Assignment Examples

“…[10]). The parameter setting of  can be directly performed by the DM (probably in a constructive framework collaborating with a decision analyst), or be inferred by using an indirect elicitation method ( [11,12]). …”

Multicriteria optimization of interdependent project portfolios with a priori incorporation of decision maker preferences

“…This can be done by an interaction between the DM and a decision analyst, utilizing, if necessary, indirect elicitation methods to support this task ( [49,50,51]). …”