论文标题
EOS：用于约束全局优化的平行，自适应，多人种进化算法
EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization
论文作者
论文摘要
本文介绍了名为EOS的进化优化代码的主要特征，Sapienza的进化优化以及其成功应用于具有挑战性的现实空间轨迹优化问题。 EOS是一种全局优化算法，用于实现变量的约束和不受约束的问题。它实现了众所周知的差分进化（DE）算法的许多改进，即控制参数的自加入，一种流行性机制，一种聚类技术，一种$ \ varepsilon $限制的方法来处理非线性约束，并处理多个同步的岛屿模型，以处理多个群体。该结果报告证明，与最先进的单人群自适应DE算法相比，当应用于高维或高度约束的空间轨迹优化问题时，能够达到性能提高。
This paper presents the main characteristics of the evolutionary optimization code named EOS, Evolutionary Optimization at Sapienza, and its successful application to challenging, real-world space trajectory optimization problems. EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm, namely, a self-adaptation of the control parameters, an epidemic mechanism, a clustering technique, an $\varepsilon$-constrained method to deal with nonlinear constraints, and a synchronous island-model to handle multiple populations in parallel. The results reported prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms when applied to high-dimensional or highly-constrained space trajectory optimization problems.
