| 引用本文： |
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谢承旺,潘嘉敏,付世炜,廖剑平.LSMOEA/2s：一种基于变量两阶段分组的多目标进化算法[J].广西科学,2023,30(2):413-420. [点击复制]
- XIE Chengwang,PAN Jiamin,FU Shiwei,LIAO Jianping.LSMOEA/2s:A Large-Scale Multi-Objective Evolutionary Algorithm Adopting Two-Stage Variable Grouping[J].Guangxi Sciences,2023,30(2):413-420. [点击复制]
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| 摘要: |
| 大规模多目标优化问题(Large-Scale Multi-objective Optimization Problem,LSMOP)固有的性质给多目标进化算法(Multi-Objective Evolutionary Algorithm,MOEA)带来挑战。目前大多数大规模多目标进化算法(Large-Scale Multi-Objective Evolutionary Algorithm,LSMOEA)需要耗费较多的计算资源对大规模决策变量进行分组，使得用于优化问题解的计算资源相对不足，影响了算法效率和解题性能。基于此，本研究提出一种基于变量两阶段分组的多目标进化算法(Large-Scale Multi-Objective Evolutionary Algorithm adopting two-stage variable grouping,LSMOEA/2s)。新算法首先利用基于变量组的相关性检测方法快速识别独立变量，然后利用高频次随机分组方法将非独立变量划分成若干子组，最后利用MOEA/D算法优化所有的独立变量和非独立变量子组。将所提算法与当前4种代表性算法(MOEA/D、CCGDE3、RVEA、S3-CMA-ES)一同在LSMOP系列测试问题上进行反转世代距离(Inverted Generational Distance,IGD)性能测试，结果表明，LSMOEA/2s较其他算法具有显著的性能优势。 |
| 关键词: 大规模决策变量 多目标优化问题 大规模多目标进化算法 两阶段分组 收敛性 多样性 |
| DOI：10.13656/j.cnki.gxkx.20230529.022 |
| 投稿时间：2022-09-24修订日期：2022-10-22 |
| 基金项目:国家自然科学基金项目(61763010)和广西自然科学基金项目(2021GXNSFAA075011)资助。 |
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| LSMOEA/2s:A Large-Scale Multi-Objective Evolutionary Algorithm Adopting Two-Stage Variable Grouping |
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XIE Chengwang1,2, PAN Jiamin1, FU Shiwei1, LIAO Jianping1
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| (1.School of Computer and Information Engineering, Nanning Normal University, Nanning, Guangxi, 530000, China;2.School of Data Science and Engineering, South China Normal University, Shanwei, Guangdong, 516600, China) |
| Abstract: |
| The inherent properties of Large-Scale Multi-objective Optimization Problem (LSMOP) bring challenges to Multi-Objective Evolutionary Algorithm (MOEA).At present,most Large-Scale Multi-Objective Evolutionary Algorithm (LSMOEA) need to consume more computational resources to group large-scale decision variables,which makes the computational resources used to optimize the problem solution relatively insufficient,affecting the efficiency and performance of the algorithm.Base on this,a Large-Scale Multi-Objective Evolutionary Algorithm adopting two-stage variable grouping (LSMOEA/2s) is proposed in this study.Firstly,the new algorithm is conducive to the correlation detection method based on variable group to quickly identify independent variables.Then,the high-frequency random grouping method is used to divide the non-independent variables into several subgroups.Finally,the MOEA/D algorithm is used to optimize all independent variables and non-independent variable subgroups.The proposed algorithm is combined with the current four representative algorithms (MOEA/D,CCGDE3,RVEA,S3-CMA-ES) to perform the inverted generational distance Inverted Generational Distance (IGD) performance test on the LSMOP series test problems.The results show that LSMOEA/2s has significant performance advantages over other algorithms. |
| Key words: large-scale decision variables multi-objective optimization problem Large-Scale Multi-Objective Evolutionary Algorithm two-stage variable grouping convergence diversity |
