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.
翻译:本文件介绍名为EOS的进化优化代码的主要特点,Sapienza的进化优化及其成功应用于具有挑战性的、真实世界空间轨迹优化问题。EOS是针对受限制和不受限制的实际价值变量问题的一种全球优化算法,对众所周知的不同进化(DE)算法进行了一些改进,即控制参数的自我调整、流行病机制、集成技术、处理非线性制约的耗资等紧凑方法,以及同时处理多种人口的同步岛屿模型。报告的结果证明,EOS在适用于高维度或高度紧张的空间轨迹优化问题时,与最先进的单一人口自适应性DE算法相比,其性能能够提高。