论文标题
用于多个目标优化的有效有效的进化算法
An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization
论文作者
论文摘要
在进化的多目标优化中，有效性是指在将其溶液融合到帕累托方面的进化算法的性能，并在整个方面将其多样化。这不是一项简单的工作，特别是对于具有三个以上目标的优化问题，被称为多个目标优化问题。在此类问题中，经典的基于帕累托的算法未能为帕累托阵线提供足够的选择压力，而最近开发的算法（例如基于分解的算法）可能难以维持有关某些问题的一系列良好分布的解决方案（例如，那些不规则帕累托阵线的问题）。在一些多目标优化器中，另一个问题是，目标数量（例如基于高频量的算法和基于移位的密度估计（SDE）方法）迅速增加了计算需求。在本文中，我们旨在解决这个问题，并开发有效，有效的进化算法（E3A），该算法可以解决各种多个目标问题。在受SDE启发的E3A中，提出了一种新型的人口维护方法，以选择环境选择程序中的高质量解决方案。我们进行了广泛的实验，并表明E3A在快速找到一组良好且良好的溶液方面的表现要比11种最先进的多个目标进化算法更好。
In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is not an easy job, particularly for optimization problems with more than three objectives, dubbed many-objective optimization problems. In such problems, classic Pareto-based algorithms fail to provide sufficient selection pressure towards the Pareto front, whilst recently developed algorithms, such as decomposition-based ones, may struggle to maintain a set of well-distributed solutions on certain problems (e.g., those with irregular Pareto fronts). Another issue in some many-objective optimizers is rapidly increasing computational requirement with the number of objectives, such as hypervolume-based algorithms and shift-based density estimation (SDE) methods. In this paper, we aim to address this problem and develop an effective and efficient evolutionary algorithm (E3A) that can handle various many-objective problems. In E3A, inspired by SDE, a novel population maintenance method is proposed to select high-quality solutions in the environmental selection procedure. We conduct extensive experiments and show that E3A performs better than 11 state-of-the-art many-objective evolutionary algorithms in quickly finding a set of well-converged and well-diversified solutions.
