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By dividing the objective space into several small regions, this paper proposes an improved NSGA-II algorithm, which updates the population in each sub-region by using non-dominated sorting and crowded distance selection operator (NSGA-II). Since performing the evolutionary operator is independent in each sub-region and the number of the individuals in a sub-region is far less than the size of the...
So far there are a number of evolutionary algorithms (EAs) applied in solving multi-objective optimization problems (MOPs), but it is very hard to evaluate the performance of a multi-objective optimization evolutionary algorithm (MOEA) especially to equably evaluate the Pareto Front (PF) when the dimension of the objective space is greater than 2. This paper has made a corresponding analysis on the...
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