A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
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The resolution of a multi-objective optimization problem involves not just a search and computation phase, capable of providing a representative sample of the Pareto-optimal front, but also a decision support process to aid the Decision Maker (DM) to progress in the learning of the trade-offs at stake in different regions of the search space. This is accomplished by integrating in the search process the DM's preferences to guide the search and limit both the cognitive effort, in assessing Pareto-optimal solutions with distinct characteristics, and the computational effort, by reducing the scope of the search according to the preferences expressed by the DM. The introduction of meaningful preference expression parameters used in the ELECTRE TRI method for sorting problems in the framework of an evolutionary algorithm is proposed. Illustrative results in an operational planning problem in electricity networks are reported.