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In order to increase accuracy predicting Pareto dominance in optimizing expensive multi-objective problems with high dimensional decision vector space, a method to identifying the equivalent and redundant dimensions in decision vector space is proposed. A kind of satisfaction degree based on the contribution of each decision component to each objective function is defined and used to determine the...
A method of predicting Pareto dominance in multi-objective optimization using pattern recognition, and the framework of Pareto dominance classifier are proposed. A kind of Bayesian classifier is preliminarily implemented. It is used to predict Pareto dominance among the candidate solutions for various typical multi-objective optimization problems. The experimental data and analysis show that the predicted...
In order to further ease the disaster of computing costs in multi-objective optimization problem, we've put forward a kind of multi-objective genetic algorithm based on clustering. The algorithm uses the fuzzy c-means clustering control the similar individuals gathered in a class and for each class construct non-dominated set with arena's principle, so that we can use faster speed to choose the non-dominated...
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