差分进化算法在无约束全局优化函数上的收敛性分析
Convergence Analysis of Differential Evolution Variants on Unconstrained Global Optimization Functions
摘要：
在本文中，我们提出了一个实证研究的收敛性质的差分进化（DE）变种解决无约束全局优化问题。目的是确定DE变体在解决其手头问题时的竞争性质并进行比较。我们选择了14个基准函数，按功能：单峰和可分离，单峰和不可分离，多峰和可分离，多峰和不可分离。DE的14个变种的实施和测试的14个基准问题的尺寸为30。通过平均目标函数值来识别变体的竞争力，它们在100次运行中实现。通过测量它们的收敛速度（Cs）和质量度量（Qm）来分析性能最好和最差的变体的收敛性质。
英文摘要：
In this paper, we present an empirical study on convergence nature of Differential Evolution (DE) variants to solve unconstrained global optimization problems. The aim is to identify the competitive nature of DE variants in solving the problem at their hand and compare. We have chosen fourteen benchmark functions grouped by feature: unimodal and separable, unimodal and nonseparable, multimodal and separable, and multimodal and nonseparable. Fourteen variants of DE were implemented and tested on fourteen benchmark problems for dimensions of 30. The competitiveness of the variants are identified by the Mean Objective Function value, they achieved in 100 runs. The convergence nature of the best and worst performing variants are analyzed by measuring their Convergence Speed (Cs) and Quality Measure (Qm).
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