基于莱维飞行的鸟群优化算法
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四川大学 电子信息学院,四川大学 电子信息学院,四川大学 电子信息学院,四川大学 电子信息学院

中图分类号:

TP301.6

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国家重点基础研究发展计划(973计划)

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    摘要:

    针对鸟群优化算法(BSA)在求解高维多极值优化问题时容易陷入局部最优解和出现早熟收敛的情况,在原始鸟群算法的基础上,在模拟鸟群飞行行为的过程中引入莱维飞行,提出了一种基于莱维飞行的改进算法——莱维-鸟群算法(LBSA)。这种算法替换了原算法中随机的飞行位置跳变,而采用莱维飞行更新鸟群飞行后的位置,大幅提高了鸟群的位置变化活力,提高了算法的有效性。仿真结果表明,在求解高维多极值优化问题时,该算法性能优于原始鸟群算法。

    Abstract:

    Considering the fact that the original Bird Swarm Algorithm(BSA) in optimizing high-dimensional multi-extreme value easily gets locally optimal solution and premature convergence, an improved algorithm, Levy-Bird Swarm Algorithm(LBSA) is proposed, which is based on Levy flight, a simulation of the birds flying. LBSA replaces the random location changes in the original algorithm by using Levy flight to update the flight locations, which substantially increases the vitality of the location changes, and makes the algorithm more effective. The results of simulation show that the LBSA outperforms the original BSA in optimizing high-dimensional multi-extreme value.

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刘晓龙,宁芊,赵成萍,涂榫.基于莱维飞行的鸟群优化算法计算机测量与控制[J].,2016,24(12):50.

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  • 收稿日期:2016-07-06
  • 最后修改日期:2016-08-09
  • 录用日期:2016-07-25
  • 在线发布日期: 2017-02-06
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