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A numerical constrained optimization method via searching for saddle points of a Lagrangian by using Artificial Bee Colony (ABC) algorithm

机译:人工蜂群算法求解拉格朗日鞍点的数值约束优化方法

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This paper proposes a constrained optimization method via numerically searching for saddle points of a Lagrangian. It is well-known that a solution for constrained optimization problems is equivalent to a saddle point of the corresponding Lagrangian. After developing a saddle points search method for nonlinear functions by using Artificial Bee Colony (ABC) algorithm, we propose its implementation for constrained optimization. In the proposed method, we additionary consider conditions to find non-stationary saddle points of the Lagrangian for inequality constrained problems. Numerical examples show the effectiveness of the proposed method.
机译:通过数值搜索拉格朗日的鞍点,提出了一种约束优化方法。众所周知,约束优化问题的解决方案等效于相应的拉格朗日方程的鞍点。在使用人工蜂群算法(ABC)开发了非线性函数的鞍点搜索方法之后,我们提出了用于约束优化的实现方法。在提出的方法中,我们还考虑了条件,以寻找不等式约束问题的拉格朗日非平稳鞍点。数值算例表明了该方法的有效性。

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