一种用于空间轨迹优化的膨胀差分进化算法
An inflationary differential evolution algorithm for space trajectory optimization
AI总结：
本文提出一种基于离散动力系统的膨胀差分进化算法，通过分析固定点收敛性，结合引导重启策略，在空间轨迹优化问题上优于标准差分进化算法。
摘要：
在本文中，我们定义了一个离散动力系统，该控制系统控制着种群中个体的演化。从该动力系统出发，推导出差分进化的一种变体。然后证明，在关于差分变异策略和目标函数局部结构的一些假设下，所提出的动力系统具有固定点，并且在无限代数的条件下以概率1收敛到这些固定点。利用这一性质，我们推导出一种算法，该算法在某些空间轨迹优化问题上比标准差分进化算法表现更好。随后，该新算法通过引导重启过程进行扩展，进一步提高了性能，降低了陷入欺骗性局部最优的概率。
英文摘要：
In this paper we define a discrete dynamical system that governs the evolution of a population of agents. From the dynamical system, a variant of Differential Evolution is derived. It is then demonstrated that, under some assumptions on the differential mutation strategy and on the local structure of the objective function, the proposed dynamical system has fixed points towards which it converges with probability one for an infinite number of generations. This property is used to derive an algorithm that performs better than standard Differential Evolution on some space trajectory optimization problems. The novel algorithm is then extended with a guided restart procedure that further increases the performance, reducing the probability of stagnation in deceptive local minima.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
