Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation
Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation
复制标题
通过问题重构加速大规模多目标优化
DOI：
10.1109/tevc.2019.2896002
复制
发表时间：
2019-12-01
影响因子：
14.3
通讯作者：
Yao, Xin
中科院分区：
文献类型：
--
作者：
He, Cheng;Li, Lianghao;Yao, Xin
在本文中，我们提出了一个框架，以加快大规模多目标优化的进化算法的计算效率。其主要思想是通过问题重构直接跟踪Pareto最优集（PS）。开始，该算法获得决策空间中的一组参考方向，并将它们与用于定位PS的一组权重变量相关联。然后将原大规模多目标优化问题转化为低维单目标优化问题。在重构后的问题中，决策空间由权重变量重构，目标空间由指标函数约简。由于权重变量的低维性和目标空间的缩减，可以有效地获得一组准最优解。最后，一个多目标进化算法被用来传播的准最优解的近似Pareto最优前沿均匀。实验已经进行了各种大规模的多目标问题，多达5000个决策变量。四种不同类型的代表性算法被嵌入到所提出的框架，并与他们的原始版本，分别进行比较。此外，所提出的框架进行了比较与两个国家的最先进的大规模多目标优化算法。实验结果表明，在大规模多目标优化问题中，该框架在性能和计算效率方面都有显著的提高。
In this paper, we propose a framework to accelerate the computational efficiency of evolutionary algorithms on large-scale multiobjective optimization. The main idea is to track the Pareto optimal set (PS) directly via problem reformulation. To begin with, the algorithm obtains a set of reference directions in the decision space and associates them with a set of weight variables for locating the PS. Afterwards, the original large-scale multiobjective optimization problem is reformulated into a low-dimensional single-objective optimization problem. In the reformulated problem, the decision space is reconstructed by the weight variables and the objective space is reduced by an indicator function. Thanks to the low dimensionality of the weight variables and reduced objective space, a set of quasi-optimal solutions can be obtained efficiently. Finally, a multiobjective evolutionary algorithm is used to spread the quasi-optimal solutions over the approximate Pareto optimal front evenly. Experiments have been conducted on a variety of large-scale multiobjective problems with up to 5000 decision variables. Four different types of representative algorithms are embedded into the proposed framework and compared with their original versions, respectively. Furthermore, the proposed framework has been compared with two state-of-the-art algorithms for large-scale multiobjective optimization. The experimental results have demonstrated the significant improvement benefited from the framework in terms of its performance and computational efficiency in large-scale multiobjective optimization.