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中文摘要：
新改进的Price算法能够求解多峰、多维，以及不可微目标函数的全局优化问题。把新改进的Price算法作为局部搜索算子，并入到实数编码遗传算法中，构成一个混合遗传算法，求解约束优化问题。该混合算法增强了全局寻优能力，提高了函数值的精度，并减少了计算量。通过对13个约束标准测试函数的仿真实验，并和已有算法的比较，结果表明本文提出的混合遗传算法是有效的。
英文摘要：
The modification to the new version of the Price＇s algorithm can find the global minimum of a multimodal, multivariate and nondiffrentiable function. In this paper, the modification to the new version of the Price＇s algorithm is taken as a local search operator, which is combined with the realcoded genetic algorithm. Thus a new hybrid genetic algorithm is proposed for constrained optimization problems. The new approach is capable of enhancing the global search ability of the genetic algorithm, improving the accuracy of the minimum function value, as well as reducing the computational burden. The hybrid genetic algorithm has been tested on 13 constrained benchmark problems. The results obtained have been compared with those of other existing algorithms. Simulation results show the effectiveness of the proposed algorithm.
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