一种基于灰预测理论的混合蛙跳算法
发布时间：2018-11-05 21:03
【摘要】：为提高混合蛙跳算法在优化问题求解中的性能,提出一种基于灰预测理论的改进混合蛙跳算法。该算法首先将基本算法的进化模式进行调整,强化了进化过程中全局信息的交换;之后引入移动步长变异算子,根据进化过程的不同阶段和利用灰预测理论获得进化过程中最优解进步速度,并借鉴模糊控制思想对该变异算子进行控制,进而实现移动步长的自适应调整。采用6个标准测试函数,与基本算法和已有改进算法进行性能对比分析,证明了改进后的混合蛙跳算法在收敛精度、收敛速度和收敛成功率方面的优越性及灰预测理论在算法改进领域中的可行性。最后,将改进算法应用于10 k V油浸式配电变压器优化设计工作中,验证了该改进算法的实用性。
[Abstract]:In order to improve the performance of hybrid leapfrog algorithm in solving optimization problems, an improved hybrid leapfrog algorithm based on grey prediction theory is proposed. Firstly, the evolutionary model of the basic algorithm is adjusted to enhance the exchange of global information in the evolution process. Then the moving step size mutation operator is introduced. According to the different stages of the evolution process and the grey prediction theory, the optimal solution progress speed is obtained, and the fuzzy control theory is used to control the mutation operator. Then the adaptive adjustment of mobile step size is realized. By using six standard test functions, the performance of the improved hybrid leapfrog algorithm is compared with that of the basic algorithm and the existing improved algorithm, and the convergence accuracy of the improved hybrid leapfrog algorithm is proved. The advantages of convergence rate and convergence success rate and the feasibility of grey prediction theory in the field of algorithm improvement. Finally, the improved algorithm is applied to the optimization design of 10 kV oil-immersed distribution transformer, and the practicability of the improved algorithm is verified.
【作者单位】： 河北工业大学电磁场与电器可靠性省部共建重点实验室;
【基金】：河北省自然科学基金(E2016202134) 河北省人社厅项目(A2013007001) 河北省科学技术研究与发展项目(13210129) 河北省高等学校创新团队领军人才培育计划项目(LJRC003)资助
【分类号】：TP18
本文编号：2313451
[Abstract]:In order to improve the performance of hybrid leapfrog algorithm in solving optimization problems, an improved hybrid leapfrog algorithm based on grey prediction theory is proposed. Firstly, the evolutionary model of the basic algorithm is adjusted to enhance the exchange of global information in the evolution process. Then the moving step size mutation operator is introduced. According to the different stages of the evolution process and the grey prediction theory, the optimal solution progress speed is obtained, and the fuzzy control theory is used to control the mutation operator. Then the adaptive adjustment of mobile step size is realized. By using six standard test functions, the performance of the improved hybrid leapfrog algorithm is compared with that of the basic algorithm and the existing improved algorithm, and the convergence accuracy of the improved hybrid leapfrog algorithm is proved. The advantages of convergence rate and convergence success rate and the feasibility of grey prediction theory in the field of algorithm improvement. Finally, the improved algorithm is applied to the optimization design of 10 kV oil-immersed distribution transformer, and the practicability of the improved algorithm is verified.
【作者单位】： 河北工业大学电磁场与电器可靠性省部共建重点实验室;
【基金】：河北省自然科学基金(E2016202134) 河北省人社厅项目(A2013007001) 河北省科学技术研究与发展项目(13210129) 河北省高等学校创新团队领军人才培育计划项目(LJRC003)资助
【分类号】：TP18
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