Axiomatic foundation of the analytic hierarchy process
Management Science
Modern Control Systems
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Fuzzy Sets and Systems - Theme: Decision and optimization
Diagnosis of the diseases: using a GA-fuzzy approach
Information Sciences: an International Journal - Special issue: Medical expert systems
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
Advances in Evolutionary Algorithms: Theory, Design and Practice (Studies in Computational Intelligence)
Fuzzy preference modeling and its application to multiobjective decision making
Computers & Mathematics with Applications
Design of a fuzzy controller in mobile robotics using genetic algorithms
Applied Soft Computing
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
Network reconfiguration of distribution system using fuzzy preferences multi-objective approach
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
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A multi-objective optimization evolutionary algorithm incorporating preference information interactively is proposed. A new nine grade evaluation method is used to quantify the linguistic preferences expressed by the decision maker (DM) so as to reduce his/her cognitive overload. When comparing individuals, the classical Pareto dominance relation is commonly used, but it has difficulty in dealing with problems involving large numbers of objectives in which it gives an unmanageable and large set of Pareto optimal solutions. In order to overcome this limitation, a new outranking relation called "strength superior" which is based on the preference information is constructed via a fuzzy inference system to help the algorithm find a few solutions located in the preferred regions, and the graphical user interface is used to realize the interaction between the DM and the algorithm. The computational complexity of the proposed algorithm is analyzed theoretically, and its ability to handle preference information is validated through simulation. The influence of parameters on the performance of the algorithm is discussed and comparisons to another preference guided multi-objective evolutionary algorithm indicate that the proposed algorithm is effective in solving high dimensional optimization problems.