6533b7dcfe1ef96bd1272a0b
RESEARCH PRODUCT
An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA
Ana Belen RuizMariano LuqueKaisa MiettinenRubén Saboridosubject
Mathematical optimizationOptimization problemMultiobjective programmingComputer scienceEvolutionary algorithmReference point approachInteractive evolutionary computationPareto optimal solutionsEvolutionary algorithmsPreference (economics)AlgorithmMulti-objective optimizationInteractive methodsdescription
In this paper, we describe an interactive evolutionary algorithm called Interactive WASF-GA to solve multiobjective optimization problems. This algorithm is based on a preference-based evolutionary multiobjective optimization algorithm called WASF-GA. In Interactive WASF-GA, a decision maker (DM) provides preference information at each iteration simple as a reference point consisting of desirable objective function values and the number of solutions to be compared. Using this information, the desired number of solutions are generated to represent the region of interest of the Pareto optimal front associated to the reference point given. Interactive WASF-GA implies a much lower computational cost than the original WASF-GA because it generates a small number of solutions. This speeds up the convergence of the algorithm, making it suitable for many decision-making problems. Its e ciency and usefulness is demonstrated with a ve-objective optimization problem. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-01-01 |