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A novel solver formulti-objective optimization: dynamic non-dominated sorting genetic algorithm (DNSGA)
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DOI：10.1007/s00500-021-06223-0.png)
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Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So, the complexity of non-dominated sorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominated sorting method called the dynamic non-dominated sorting (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-dominated sorting methods. Based on DNS, we introduce a novel multi-objective genetic algorithm 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.
Keyword:
Dynamic non-dominated sorting genetic algorithm
Multi-objective optimization
Multi-objective evolutionary algorithm
Test problems
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2.5
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被引数:
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Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons进化动态多目标优化: 基准和算法比较
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