基于自适应惯性权重的樽海鞘算法
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兰州交通大学

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TP301.6

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国家自然科学基金资助项目(51768035);甘肃省高校协同创新团队项目(2018C-12);


Salp Swarm Algorithm Based on Adaptive Inertia Weight
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Lanzhou Jiaotong University

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    摘要:

    针对标准樽海鞘群算法收敛精度低、收敛速度慢的问题, 提出一种基于自适应惯性权重的樽海鞘群算法(AIWSSA). 首先, 在追随者位置更新公式中引入惯性权重因子评价个体之间的影响程度; 然后, 结合种群成功率与非线性递减函数对惯性权重因子进行自适应调整, 使算法的全局和局部搜索能力得到更好地平衡; 最后, 为防止算法陷入局部最优, 引入差分变异思想对非最优个体进行变异. 对12 个基准测试函数进行求解, 实验结果表明: AIWSSA具有较高的收敛精度、收敛速度和鲁棒性; Wilcoxon 统计检验结果表明: 与标准樽海鞘群算法、改进的樽海鞘群算法、其他群体智能算法相比, AIWSSA表现出较好的性能. 通过将其应用于两种带约束的工程设计问题,验证了AIWSSA的有效性.

    Abstract:

    The standard salp swarm algorithm (SSA) has low convergence accuracy and slow convergence speed. In order to solve these problems, a salp swarm algorithm based on adaptive inertia weight (AIWSSA) is proposed. Firstly, the inertia weight factor is introduced into the follower position update formula to evaluate the degree of influence between the individuals. Secondly, the combination of population successful rate and nonlinear decreasing function is applied to adjust the inertia weight factor adaptively to balance the exploration and exploitation abilities of the proposed algorithm. Finally, the differential mutation for the non-optimal individuals is used to avoid the algorithm of being trapped into local optimum. Then the experiments on the 12 benchmark test functions are conducted. The results show that the proposed AIWSSA has higher convergence accuracy, convergence speed and robustness, and the Wilcoxon statistical test results demonstrate that it has better performance compared with the standard salp swarm algorithm, the improved salp swarm algorithms and other swarm intelligence algorithms. Two constrained engineering design problems are applied to verify the effectiveness of the algorithm.

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历史
  • 收稿日期:2020-04-21
  • 最后修改日期:2021-07-19
  • 录用日期:2020-11-20
  • 在线发布日期: 2020-12-01
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