non-dominatedsorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominatedsorting. So, the complexity of non-domi...
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non-dominatedsorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominatedsorting. So, the complexity of non-dominatedsorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominatedsorting method called the dynamicnon-dominatedsorting (DNS). It is based on the sorting of real number sequence instead of dominance comparisons. The computational complexity of DNS is O(mN log N) (m is the number of objectives, N is the population size), which equals to the best record so far. In the numerical experiments, we verify the outperformance of DNS comparing with other non-dominatedsorting methods. Based on DNS, we introduce a novel multi-objective geneticalgorithm called the dynamic non-dominated sorting genetic algorithm (DNSGA). Numerical experiments on DNSGA are also given. The results show that DNSGA outperforms some other MOEAs on both general-scale and large-scale multi-objective problems.
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