0000000000452858

AUTHOR

Ernestas Filatovas

0000-0002-9329-6431

showing 2 related works from this author

A visualization technique for accessing solution pool in interactive methods of multiobjective optimization

2015

<pre>Interactive methods of <span>multiobjective</span> optimization repetitively derive <span>Pareto</span> optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the <span>Pareto</span> optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the sol…

multidimensional scalingMathematical optimizationOptimization problemComputer Networks and CommunicationsComputer sciencevisualisointiPareto front visualizationcomputer.software_genreMulti-objective optimizationSet (abstract data type)menetelmätMultidimensional scalingMultiobjective optimizationdimensionality reductionFlexibility (engineering)pareto-tehokkuusDimensionality reductionMultiobjective optimization ; interactive methods ; Pareto front visualization ; dimensionality reduction ; multidimensional scalinginteractive methodsNIMBUSmonitavoiteoptimointiComputer Science ApplicationsVisualizationComputational Theory and MathematicsFeature (computer vision)interaktiivisuusData miningcomputer
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Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

2015

Classical evolutionary multi-objective optimization algorithms aim at finding an approx- imation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended preference-based evolutionary algorithm has been proposed for solving multi-objective optimiza- tion problems. Here, concepts from an interactive synchronous NIMBUS method are borrowed and combined with the R-NSGA-II algorithm. The proposed synchronous R-NSGA-II algorithm uses preference information provid…

ta113Mathematical optimizationinteractive multi-objective optimizationApplied MathematicsEvolutionary algorithmApproxDecision makerMulti-objective optimizationscalarizing functionSet (abstract data type)Pareto optimalevolutionary multi-objective optimizationpreference-based evolutionary algorithmsFocus (optics)Preference (economics)Information SystemsMathematicsInformatica
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