状态转移算法中算子选择的统计研究
作者:
作者单位:

新疆大学

作者简介:

通讯作者:

中图分类号:

TP301

基金项目:

国家自然科学基金项目(51767022、51967019)


A statistical study on operator selection in state transition algorithm
Author:
Affiliation:

Xinjiang University

Fund Project:

National Natural Science Foundation of China(51767022,51967019)

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    摘要:

    针对基本状态转移算法(State transition algorithm,\,STA)搜索效率低和易陷入局部最优的不足,本文对不同算子求解特定优化问题的效果差异性展开统计研究,提出一种带有策略自适应的状态转移算法(SaSTA).首先,定义了成功率和下降率两个指标,并在3个测试函数上进行统计研究,以证明不同算子对算法搜索能力的影响,设计了一种综合成功率和下降率的评价指标对最优算子进行自适应选择;其次,采用一种非线性控制参数策略来平衡算法的探索和开发能力;最后,将所提算法应用于15个基准测试函数(100维、300维和500维).仿真结果表明,所提算法在求解精度、收敛速度和稳定性方面都明显优于其他对比算法.

    Abstract:

    In view of the shortcomings of basic state transition algorithm (STA) such as low search efficiency and easy to fall into local optimum. Based on the statistical study of the difference of the effects of different operators in solving specific optimization problems, a state transition algorithm with strategy adaptation (SaSTA) is proposed. Firstly, two indexes of success rate and descent rate are defined, and statistical studies are conducted on three test functions to prove the influence of different operators on the search capability of the algorithm, and an evaluation index of comprehensive success rate and descent rate is designed to adaptively select the optimal operator. Secondly, a nonlinear control parameter strategy is adopted to balance the exploration and exploitation ability of the algorithm. Finally, the proposed algorithm is applied to 15 benchmark functions (100, 300 and 500 dimension). The simulation results show that the proposed algorithm is superior to other comparative algorithms in terms of solution precise, convergence speed and stability.

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历史
  • 收稿日期:2020-11-11
  • 最后修改日期:2021-01-08
  • 录用日期:2021-01-19
  • 在线发布日期: 2021-02-04
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