An Improved NSGA-II algorithm for multi-objective nonlinear optimization

ZHENG Xia; Ma Liang

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An Improved NSGA-II algorithm for multi-objective nonlinear optimization

更新时间：2026-09-04

- An Improved NSGA-II algorithm for multi-objective nonlinear optimization
- Vol. 37, Issue 7, Pages: 47-53(2020)
- 作者机构：
  
  上海理工大学机构管理学院,上海,200093
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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郑夏, 马良. 一种多目标非线性优化的NSGA-II改进算法[J]. 微电子学与计算机, 2020, 37(7): 47-53.

ZHENG Xia, Ma Liang. An Improved NSGA-II algorithm for multi-objective nonlinear optimization[J]. 2020, 37(7): 47-53.
郑夏, 马良. 一种多目标非线性优化的NSGA-II改进算法[J]. 微电子学与计算机, 2020, 37(7): 47-53. DOI：

ZHENG Xia, Ma Liang. An Improved NSGA-II algorithm for multi-objective nonlinear optimization[J]. 2020, 37(7): 47-53. DOI：

摘要

引入排序算法、拥挤度算子和精英策略后的非支配排序遗传算法(NSGA-II)，它在多目标优化领域具有广泛的应用，但也存在个体分布不均，算法中的Pareto效率降低等问题.针对这些缺陷进行算法改进，首先在提出一种累积非支配排序赋值策略的同时改进交叉算子，利用具有自适应参数的DE优化思想对群体、初始群体的多样性分布进行了改进；通过融入非线性优化策略，提高了局部搜素能力，进一步提高了Pareto最优解的质量.最后，测试了六个标准多目标函数进行算法比较.测试结果表明所提的NDE-NSGA-II算法具有更好的分布性，稳定性和更高的搜索解能力.

Abstract

Non-dominated sorting genetic algorithm (NSGA-II) has been widely used in the field of multi-objective optimization after introducing sorting algorithm

crowding degree operator and elite strategy

but there are also problems such as uneven distribution of individual and Pareto efficiency. The algorithm is improved for these defects. Firstly

a cumulative non-dominated ranking assignment strategy is proposed to improve the crossover operator. The DE algorithm with adaptive parameters is used to improve the initial population and the population diversity; Then introduce the idea of nonlinear algorithm to improve the local search ability and further improve the quality of Pareto optimal solution. Finally

six benchmark multi-objective functions are used for testing. The experimental results show that the improved NDE-NSGA-II algorithm has better distribution

stability and higher search solution.

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references

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