基于教与学策略的动态变异花授粉算法
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国家自然科学基金(61972184, 61562032); 河池学院高层次人才科研启动项目(2019GCC012)


Dynamically Mutant Flower Pollination Algorithm Based on Teaching and Learning Strategies
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    摘要:

    为了进一步提升花授粉算法的优化性能, 本文提出一种融入改进的教与学优化策略及动态高斯变异的新花授粉算法. 该算法先用教机制中改进的教学因子得到的最优个体与其他个体间的促进作用来提高算法的收敛速度; 同时运用种群个体之间相互学习的学机制来保持种群的多样性, 从而提升算法的优化精度; 然后, 当检测到算法陷入早熟时, 则对种群的中间个体进行动态高斯变异, 增加个体之间的差异性, 避免算法早熟, 进而提升算法的综合优化能力. 通过对16个标准函数的优化结果实验和非参数统计检验分析对比, 证明了该算法的有效性; 并与其他改进的花授粉算法进行比较分析, 结果显示本文算法优势较显著. 最后, 运用新算法对伸缩绳应用问题进行求解, 亦获得较好的优化结果.

    Abstract:

    This study proposes a new flower pollination algorithm by incorporating the improved teaching-learning-based optimization strategy and dynamic Gaussian mutation to enhance the optimization performance. The algorithm first speeds the convergence through the promotion effect between the optimal individual and other individuals obtained by the improved teaching factor in the teaching mechanism. At the same time, the mutual learning mechanism between individuals is adopted to maintain the diversity of the population, thereby improving the optimization accuracy. Then, when it is detected that the algorithm falls into prematurity, the dynamic Gaussian mutation is carried out on the middle individuals of the population to increase the differences between individuals. In this way, it avoids the prematurity of the algorithm and then improves the comprehensive optimization ability. The optimization results of 16 standard functions are checked by the nonparametric statistical test to prove the effectiveness of the algorithm. Compared with other improved pollination algorithms, this algorithm has significant advantages. Finally, the new algorithm is applied to solve the application problems of telescopic rope, and good optimization results are achieved.

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段艳明,肖辉辉,谭黔林,赵翠芹.基于教与学策略的动态变异花授粉算法.计算机系统应用,2022,31(10):142-155

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  • 收稿日期:2022-01-04
  • 最后修改日期:2022-01-30
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  • 在线发布日期: 2022-07-07
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