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目录
- 1.算法原理
- 2.改进点
- 3.结果展示
- 4.参考文献
- 5.代码获取
1.算法原理
【智能算法】麻雀搜索算法(SSA)原理及实现
今天复现一篇论文:一种多混合策略改进的麻雀搜索算法及其在TSP中的应用(A Multimixed Strategy Improved Sparrow Search Algorithm and Its Application in TSP)
2.改进点
Iterative Chaotic Map Strategy
Singer 混沌映射产生的混沌变量在 0.6 到 0.9 之间分布不均,并且周期性较小。Tent混沌映射在 0 到 0.2 之间有周期性不稳定的现象,并且容易陷入固定点。Sinusoidal混沌映射产生的混沌变量具有一定的双峰分布特性,在混沌吸引域的中间分布较均匀,在两端密集分布。这里,采用迭代混沌映射策略来初始化种群采用迭代混沌映射:
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(1)
x_{i+1}=\sin\!\left(\frac{b\pi}{x_i}\right)\tag{1}
xi+1=sin(xibπ)(1)
Golden Sine Algorithm Strategy
引入黄金正弦策略(Golden Sine Algorithm Strategy)和非线性因子(Nonlinear Convergence Factor Strategy)改进发现者位置:
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(2)
\left.X_{i,j}^{t+1}=\left\{\begin{array}{ll}X_{i,j}^t\lvert\sin\left(r_1\right)\rvert-r_2\sin\left(r_1\right)\Big|\theta_1\cdot X_p^t-\theta_2\cdot X_{i,j}^t\Big|,&R_2<ST\\\\X_{i,j}^t+\omega\cdot L,&R_2\geq ST\end{array}\right.\right.\tag{2}
Xi,jt+1=⎩
Elite Opposition-Based Learning Strategy
采用精英对立学习策略对前10%麻雀位置进行扰动:
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\overline{X_{i,j}^{e}}=\lambda\times\bigl(lb_{j}+ub_{j}\bigr)-X_{i,j}^{e}\tag{3}
Xi,je=λ×(lbj+ubj)−Xi,je(3)
其中,参数表述为:
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lb_{j}=\min{(X_{i,j})}, ub_{j}=\max{(X_{i,j})}\tag{4}
lbj=min(Xi,j),ubj=max(Xi,j)(4)
3.结果展示
TSP应用
测试TSP数据集 burma14,eil51
4.参考文献
[1] Li W, Zhang M, Zhang J, et al. A Multimixed Strategy Improved Sparrow Search Algorithm and Its Application in TSP[J]. Mathematical Problems in Engineering, 2022, 2022(1): 8171164.
5.代码获取
本文标签: 算法智能StrategyMultimixedImproved
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