6533b852fe1ef96bd12aac58

RESEARCH PRODUCT

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Olga KurasovaErnestas FilatovasKarthik Sindhya

subject

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 SystemsMathematics

description

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 provided by the decision maker to find only desirable solutions satis- fying his/her preferences on the Pareto front. Several scalarizing functions are used simultaneously so the several sets of solutions are obtained from the same preference information. In this paper, the experimental-comparative investigation of the proposed synchronous R-NSGA-II and original R-NSGA-II has been carried out. The results obtained are promising.

yearjournalcountryeditionlanguage
2015-01-01Informatica
https://doi.org/10.15388/informatica.2015.37