Assessing the quality of the relation between scalarizing function parameters and solutions in multiobjective optimization

Authors:
José C. Ferreira;Carlos M. Fonseca;António Gaspar-Cunha
Affiliations:
Institute for Polymers and Composites, University of Minho, Guimarães, Portugal;Faculty of Science and Technology, Universidade do Algarve, Faro, Portugal and CEG-IST, Instituto Superior Técnico, Universidade Técnica de Lisboa, Portugal;Institute for Polymers and Composites, University of Minho, Guimarães, Portugal
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 6
Cited 1

What can we learn from experiments in multiobjective decision analysis?

IEEE Transactions on Systems, Man and Cybernetics
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review

IEEE Transactions on Evolutionary Computation
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Aesthetic design using multi-objective evolutionary algorithms

EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

Different Multi-Objective Optimization Methods (MOOM) for solving Multi-Objective Optimization Problems (MOOP) have been suggested in the literature. These methods often comprise two stages (not necessarily sequential): i) the search for the Pareto-optimal set and ii) the selection of a single solution from this non-dominated set. Various studies comparing performance of particular aspects of these methods have been carried out. However, a theoretical support that changes on the preferences of a Decision Maker (DM) will be reflected in the same way on the solution of the MOOP given by the MOOM has not been presented. In this work a consistency measure to assess MOOM is proposed. It will used to compare the performance of different methods available in the literature. This study was performed using some benchmark test problems, with two criteria.