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.

中文翻译：

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EOS：一种用于约束全局优化的并行、自适应、多种群进化算法

本文介绍了名为 EOS 的进化优化代码（Sapienza 的进化优化）的主要特征，及其在具有挑战性的现实世界空间轨迹优化问题中的成功应用。EOS 是一种用于实值变量的有约束和无约束问题的全局优化算法。它对著名的差分进化 (DE) 算法进行了许多改进，即控制参数的自适应、流行机制、聚类技术、处理非线性约束的 $\varepsilon$ 约束方法，以及一个同步岛模型来并行处理多个种群。