An Evolutionary Algorithm with Double-Level Archives for Multi-Objective Optimization
An Evolutionary Algorithm with Double-Level Archives for Multi-Objective Optimization
复制标题
一种用于多目标优化的双层档案进化算法
DOI：
10.1109/tcyb.2014.2360923
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发表时间：
--
影响因子：
11.8
通讯作者：
Yun Li
中科院分区：
文献类型：
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作者：
Ni Chen;Wei-Neng Chen;Yue-Jiao Gong;Zhi-Hui Zhan;Jun Zhang;Yu-Song Tan;Yun Li
现有的多目标进化算法（MOEAs）处理多目标问题作为一个整体或作为几个分解的单目标子问题。虽然问题分解方法通常通过同时优化所有子问题而更快地收敛，但是存在两个问题没有完全解决，即，解的分布通常取决于先验问题分解，以及子问题之间缺乏群体多样性。本文设计了一个具有两级档案的MOEA。该算法通过引入两种类型的档案，即，全局存档和子存档。在每一代人中，与全球档案馆的自我复制以及全球档案馆与子档案馆之间的交叉复制都会产生新的个体。全球档案和分档案通过交叉复制进行交流，并利用复制的个人进行更新。因此，这样的框架保持快速收敛，并在同一时间处理解决方案分布沿着帕累托前沿（PF）与可扩展性。为了测试所提出的算法的性能，广泛使用的基准测试和一组真正断开的问题进行了实验。结果表明，与现有的MOEA算法相比，该算法在距离PF、解覆盖率和搜索速度等方面具有明显的优势。
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed.