| Summary: | 碩士 === 國立臺灣海洋大學 === 資訊工程學系 === 107 === Evolutionary multi-objective optimization (EMO) plays a leading role in solving many problems with multiple conflicting objectives due to their ability to find a set of nondominated solutions near the Pareto optimal front. But before applying a selection algorithm, the size of this nondominated solution set might be big or even unlimited. Then how shall we choose from this set of solutions nondominated to each other? We go further and choose solutions that are more unique. The more diverse the final solutions are, the better these solutions are for reference.
This paper aims to optimize the step for selecting final solutions from the candidate nondominated solutions set or the step where NSGA-II combines the parent and child population set and makes a new generation from half of them. We hope to achieve higher precision with more computation time. By knowing the number of final solutions needed, we divide the whole nondominated front into equal parts, and we select solutions closest to the boundary line. Our algorithm works by appending it to other multiobjective optimization algorithms, we also uses the fast non-dominated sorting from NSGA-II to sort out the solutions because finding solutions near the pareto optimal front is our first priority, then we apply our selection algorithm.
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