基于自适应正态云模型的引力樽海鞘群算法
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湖南科技大学信息与电气工程学院

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TM352

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Gravity Salp Swarm Algorithm Based on Adaptive Normal Cloud Model
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College of Information and Electrical Engineering,Hunan University of Science and Technology

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    摘要:

    针对樽海鞘群算法(Salp Swarm Algorithm,SSA)在求解复合问题时存在收敛速度慢和容易陷入局部最优等缺点,提出了一种结合引力搜索技术和正态云发生器的樽海鞘群算法(Cloud Gravitational SSA,CGSSA)。在更新樽海鞘领导者位置阶段引入引力搜索算法(Gravitational Search Algorithm,GSA)中的加速度系数,避免樽海鞘群的无效搜索从而加快搜索速度;使用正态云模型对樽海鞘追随者位置进行更新,丰富了种群的多样性;同时正态云模型熵值能随着迭代次数增加自适应调整,有效地提高了迭代后期的收敛精度。在23个基准函数上进行了CGSSA和其他10种优化算法的综合比较。仿真实验的统计结果、箱线图和收敛曲线表明,改进后的算法在搜索效率、收敛精度和避免局部最优方面具有更好的性能。

    Abstract:

    Aiming at the disadvantages of salp swarm algorithm (SSA), such as slow convergence speed and easy to fall into local optima when solving complex problems, an improved SSA equipped with gravitational search technique and normal cloud generator (CGSSA) is proposed. The acceleration coefficient of Gravitational search algorithm (GSA) is introduced in the stage of updating the position of the leader of salp, which avoids the invalid search of salp swarm and accelerates the search speed. The normal cloud model is used to update the position of the followers of the salp, which enriches the diversity of the population; At the same time, the entropy value of normal cloud model can be adaptively adjusted with the increase of iteration times, which effectively improves the convergence accuracy in the later iteration period. A comprehensive comparison between CGSSA and other 10 optimization algorithms is made on 23 benchmark functions. The statistical result, convergence curve and box–whisker plot of simulation experiment show that the improved algorithm has better performance in search efficiency, convergence accuracy and avoiding local optimum.

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历史
  • 收稿日期:2020-09-15
  • 最后修改日期:2020-10-26
  • 录用日期:2020-11-05
  • 在线发布日期: 2020-12-02
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