[1]于坤杰,杨振宇,乔康加,等.自适应两阶段大规模约束多目标进化算法[J].郑州大学学报(工学版),2023,44(05):1-9.[doi:10.13705/j.issn.1671-6833.2023.05.006]
　YU Kunjie,YANG Zhenyu,QIAO Kangjia,et al.Adaptive Two-stage Large-scale Constrained Multi-objective Evolutionary Algorithm[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):1-9.[doi:10.13705/j.issn.1671-6833.2023.05.006]
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自适应两阶段大规模约束多目标进化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年05期
页码:
1-9
栏目:
出版日期:
2023-08-20

文章信息/Info

Title:
Adaptive Two-stage Large-scale Constrained Multi-objective Evolutionary Algorithm
作者:
于坤杰;  杨振宇;  乔康加;  梁 静;  岳彩通
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
YU Kunjie;  YANG Zhenyu;  QIAO Kangjia;  LIANG Jing;  YUE Caitong
关键词:
大规模约束多目标优化;  算法;  自适应;  存档集;  帕累托前沿;  收敛速度;  测试函数
Keywords:
large-scale constrained multi-objective optimization;  algorithm;  self-adaption;  archive set;  Pareto front;  convergence rate;  test function
分类号:
TP301
DOI:
10.13705/j.issn.1671-6833.2023.05.006
文献标志码:
A
摘要:
针对求解大规模约束多目标优化问题时遇到的收敛速度慢和可行解难以找到的困难,提出了一种自适应 两阶段大规模约束多目标进化算法。 首先,算法在第一阶段根据决策变量的性质,自适应地选择部分变量进行优 化,且不考虑任何约束使种群快速跨过不可行区域,逼近无约束帕累托前沿。 其次,算法在第二阶段考虑全部的约 束,利用 ε 约束处理技术对变量进行整体优化;同时,利用存档将进化过程中获得的可行且非支配的解保存并更 新,以不断地提高种群的收敛性与多样性。 最后,将所提算法与其他 6 种算法在 37 个测试函数上进行实验对比, 结果表明:所提算法在 25 个函数上取得了最佳结果,且分别至少在 31 个函数上优于对比算法;所提算法在 90%以 上函数中的可行率都能达到 100%,可以有效地解决大规模约束多目标优化问题。
Abstract:
To address the difficulties of slow convergence and difficulty in finding feasible solutions when solving large-scale constrained multi-objective optimization problems, an adaptive two-stage large-scale constrained multiobjective evolutionary algorithm was proposed. In the first stage, the algorithm adaptively selected some variables for optimization according to the nature of the decision variables, without considering any constraint to make the population quickly cross the infeasible region and approach the unconstrained Pareto front. In the second stage, the algorithm considered all the constraints and optimizes the variables as a whole using the ε constraint-handling technique. At the same time, the feasible and non-dominated solutions obtained in the evolutionary process were saved and updated using archive to continuously improve the convergence and diversity of the population. Finally, the proposed algorithm was experimentally compared with the other six algorithms on 37 test functions, and the results showed that the proposed algorithm could achieved the best results on 25 functions and outperforms the comparison algorithm on at least 31 functions, respectively; meanwhile, the feasibility rate of the proposed algorithm in more than 90% of the functions could reach 100%, which could effectively solve large-scale constrained multi-objective optimization problems.

参考文献/References:

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更新日期/Last Update: 2023-09-03