• ISSN 1006-3080
• CN 31-1691/TQ

 引用本文: 王学武, 高进, 陈三燕, 顾幸生. 基于Pareto支配的两阶段多目标优化算法[J]. 华东理工大学学报（自然科学版）.
WANG Xuewu, GAO Jin, CHEN Sanyan, GU Xingsheng. Two Stage Multi-Objective Optimization Algorithm Based on Pareto Dominance[J]. Journal of East China University of Science and Technology. doi: 10.14133/j.cnki.1006-3080.20210530001
 Citation: WANG Xuewu, GAO Jin, CHEN Sanyan, GU Xingsheng. Two Stage Multi-Objective Optimization Algorithm Based on Pareto Dominance[J]. Journal of East China University of Science and Technology.

• 中图分类号: TP301

## Two Stage Multi-Objective Optimization Algorithm Based on Pareto Dominance

• 摘要: 针对二维和三维的多目标优化问题，提出了一种基于Pareto支配的两阶段多目标优化算法(MOEA-PT)。全局搜索阶段根据Pareto支配关系将种群进行排序，依据临界层子集的排序等级进行相应的选择策略。局部调整阶段对种群中的个体进行微调，将新产生的个体与其距离最近的个体进行支配关系以及分布性与收敛性的对比，替换较差的个体。分析了两个阶段对算法性能的影响，同时对引入局部调整后的种群进行了对比，结果表明局部调整策略能有效增强算法性能。通过对标准测试函数的求解，并与其他经典的多目标算法进行对比，验证了本文算法在收敛性和分布性等方面具有一定的优越性。

• 图  1  非支配排序

Figure  1.  Non-dominated sorting

图  2  ${F}_{l}$排序等级大于1的环境选择

Figure  2.  Environmental selection with Fl ranking level greater than one

图  3  ${F}_{l}$排序等级等于1的环境选择

Figure  3.  Environment selection with Fl ranking level equal to one

图  4  非支配解集

Figure  4.  Non-dominated solution sets

图  5  3种算法在部分测试函数的Pareto前沿

Figure  5.  Pareto fronts of three algorithms on some test functions

图  6  IGDm变化曲线

Figure  6.  Change curves of IGDm

图  7  局部调整前后的Pareto前沿

Figure  7.  Pareto front before and after local adjustment

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##### 出版历程
• 收稿日期:  2021-05-30
• 网络出版日期:  2021-07-26

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