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- W4292103454 abstract "Evolutionary multi-objective optimization plays a vital role in solving many complex real-world optimization problems. Numerous approaches have been proposed over the years, and popular methods such as NSGA and its variants incorporate non-dominated sorting selection into evolutionary genetic algorithms to extract competing Pareto-optimal solutions from all over the objective space. However, in applications where the decision-maker is interested in a region of interest, a global optimization wastes effort to find irrelevant solutions outside of the preferred region. In this work, we propose an approach named ROI-NSGA-II to limit the optimization effort to a region of interest defined by the boundaries provided by the decision-maker. The ROI-NSGA-II invites the classical NSGA-II algorithm into the desired region using a modified dominance relation and conquers solutions within this region using a modified crowding distance based selection. The effectiveness of our approach is demonstrated on a set of benchmark problems with up to ten objectives and a real-world application, and the results are compared to a state-of-the-art R-NSGA-II." @default.
- W4292103454 created "2022-08-17" @default.
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- W4292103454 date "2022-07-08" @default.
- W4292103454 modified "2023-09-23" @default.
- W4292103454 title "Region of interest based non-dominated sorting genetic algorithm-II" @default.
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- W4292103454 doi "https://doi.org/10.1145/3512290.3528872" @default.
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