基于方形邻域和随机数组的哈里斯鹰优化算法
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华南理工大学

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TP301.6

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促进中国中小企业研发的多层次创新政策组合研究


Harris Hawk Optimization Algorithm Based on Square Neighborhood and Random Array
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South China University of Technology

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    摘要:

    针对哈里斯鹰优化算法的探索能力和开发能力不平衡问题,通过设置一种多子群的方形邻域拓扑结构来引导各子群内的个体可以纵横双向随机觅食。为了避免局部最优,通过设置固定置换概率,来加强各个子群个体的信息交流,让子群内个体依照随机数组与其他子群的相应个体进行置换。在子群内部,基于历史进化信息进行HHO算法中的算子选择,以更好的利用现有问题领域的信息。利用可变维度基准函数与各种智能优化算法及其改进方法进行跨文献对比,结果表明改进方法在收敛精度、寻优能力上明显高于原始算法和对比文献,且具有较好的鲁棒性,适合推广至实际的优化问题之中。

    Abstract:

    Aiming at the problem of the imbalance between the exploration and development capabilities of the Harris Hawk optimization algorithm(HHO), a multi-subgroup square neighborhood topology is set up to guide individuals in each subgroup to forage randomly in both directions. In order to avoid local optima, a fixed replacement probability is set to strengthen the information exchange of each subgroup individual, so that the individuals in the subgroup can be replaced with the corresponding individuals of other subgroups according to the random array. Within the subgroup, the operator selection in the HHO algorithm is performed based on historical evolution information to make better use of the information in the existing problem domain. Cross-document comparisons are made using variable-dimensional benchmark functions with various intelligent optimization algorithms and their improved methods. The results show that the improved method is significantly higher than the original algorithm and comparative literature in terms of convergence accuracy and optimization capability, and has better robustness. , Suitable for generalization to actual optimization problems.

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历史
  • 收稿日期:2021-03-22
  • 最后修改日期:2021-06-26
  • 录用日期:2021-07-05
  • 在线发布日期: 2021-08-01
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