Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces
Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces
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DOI：
10.1023/a:1008202821328
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发表时间：
1997-12-01
影响因子：
1.8
通讯作者：
Price, K
中科院分区：
文献类型：
--
作者：
Storn, R;Price, K
提出了一种新的启发式方法，用于极小化可能的非线性和不可微的连续空间函数。通过一个广泛的测试平台，它表明，新的方法收敛速度更快，更确定性比许多其他著名的全局优化方法。新方法需要较少的控制变量，是强大的，易于使用，并非常适合于并行计算。
A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.