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Handling The Multiplicity of Solutions in a Moea Based Pda-Theseus Framework for Multi-Criteria Sorting

Covantes E., Fernández E., Navarro J.

Foundations of Computing and Decision Sciences

2016

Vol. 41, No. 4

213--235

This paper proposes the combination of the THESEUS multi-criteria sorting method with an evolutionary optimization-based preference-disaggregation analysis. The main features of the combined method are studied by performing an extensive computer experiment that explores many models of preferences and sizes of problems as well as different degrees of decision-maker involvement. As a result of the experiment, the effectiveness of the combined framework and the importance of the decision-maker’s involvement are characterized.

An improved method for deriving final ranking from a fuzzy preference relation via multiobjective optimization

Fernández E., Olmedo R.

Foundations of Computing and Decision Sciences

2003

Vol. 28, No. 3

143-157

Methods for deriving final ranking from a fuzzy preference relation do not perform well in presence of irrelevant alternatives or in case of complex graphs with numerous circuits. Recently some approaches based on the idea of reducing differences between a global model of preferences and a final ranking via multiobjective optimization with an evolutionary algorithm have been proposed. In this work a new method is presented based on similar ideas but improving them. The multiobjective optimization problem is separated into two steps and solved with a better model of preferences, also using an evolutionary algorithm simpler than the former. These improvements allow us to obtain better compromise solutions in a simpler way than the previous proposals.