【差分演化算法相关文献总结】

差分演化算法相关文献总结

前言
概述
文献综述
总结

前言

本人作为一名从事了三年演化算法研究的菜鸡研究生，其中大部分时间都在专注于差分演化算法（Differential Evolution, DE）的相关研究。现如今已经毕业，回顾往昔，经过阅读大量的文献，也算是浅浅的入了演化算法的门。
本文将总结出我在读研期间所收集和阅读过的与 DE 相关的一些论文，以供从事演化算法研究，尤其是 DE 算法研究的各位学者们进行学习和参考。下面我会附上论文的名称和对应的连接，感兴趣的小伙伴可自行下载阅读。
首先，我们还是先附上 DE 原文:
Storn R, Price K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization, 1997, 11: 341-359.

概述

在众多的演化算法中，差分演化算法作为一种经典、高效的元启发算法，具有参数少、收敛快、鲁棒性高等优点，使其一度成为了演化计算领域的热点。在2006年至2009年由IEEE举办的CEC演化大赛中，DE连续取得了第一的名次，并且在近三年的竞赛中，DE依旧具有更好的竞争力。DE是由Storn于1995所提出的一种具有较强鲁棒性的优化算法，通过基于种群的随机搜索方式来进行演化更新，每一代的个体都会经历突变、交叉和选择操作，从而将种群不断向全局最优引导。由于其具有较强的鲁棒性和简单性，DE进化已成功地应用于医疗问题优化、工程设计、路径规划、计算机视觉等各种领域中，并取得了显著的效果。
虽然DE存在许多的优点且受到了广泛的使用，但是该算法依旧存在较多的问题和提升空间，如较多的参数设置、演化寻优的随机性较大、种群多样性的丧失、算法易陷入局部最优、算法早熟、搜索停滞等现象。此类问题的出现会降低DE的算法性能，在解决实际优化问题时会造成不同程度的影响。所以，受到实际问题的驱动，对DE算法的改进和优化从未停止，众多研究者对DE存在的问题进行了讨论及优化。现如今，已有大量的DE变体被提出，极大程度的提高了算法的性能。

在这里插入图片描述

文献综述

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