一种自适应拟牛顿-状态转移混合优化算法及应用
作者:
作者单位:

中南大学

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中图分类号:

TP273

基金项目:

国家自然科学基金面上项目(61873285),国家自然科学基金国际(地区)合作与交流重点项目(61860206014),湖南省自然科学基金(2018JJ3683)


A Hybird State Transition Optimization Algorithm Based on Adaptive Quasi-Newton Method and Its Application
Author:
Affiliation:

Central South University

Fund Project:

he Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No. 61860206014),the National Natural Science Foundation of China (Grant No. 61873285), the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3683).

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    摘要:

    基本状态转移算法在某些复杂高维函数寻优后期表现出收敛慢、精度低的问题,为此引入局部搜索拟牛顿算子,构造一种混合状态转移算法,弥补了状态转移算法后期搜索效率低和拟牛顿法对初始点敏感的不足,保证算法能够快速收敛到全局或精度较高的近似最优解. 同时,混合算法使用自适应调用策略,判断算法收敛到全局最优附近的时机,并在此时调用拟牛顿算子,最大程度上发挥其局部搜索能力强的优势;并且,在算法收敛到全局最优或者近似最优解附近时,不再进行无用的拟牛顿局部搜索,节省计算资源. 通过对典型测试函数的仿真与无线传感器网络定位问题的求解,验证混合智能优化算法的有效性,且与其它群智能算法相比,混合算法具有更高的收敛速度与精度.

    Abstract:

    In order to solve the problem that the basic state transition algorithm shows slow convergence speed and low convergence accuracy in some complex high dimensional functions, a hybird state transition algorithm is proposed, which could improve the local search ability of the algorithm and accelerate the convergence speed of the algorithm by adding a local search quasi-Newton operator. Besides, a strategy is proposed to call the quasi-Newton operator adaptively, which could judge the time when the algorithm converges to the vicinity of the global optimum, and then calls the quasi-newton operator to give full play to its advantages of strong local search ability. The proposed method is successfully applied to the wireless network sensor location. Compared with other intelligent optimization algorithms, the hybird intelligence has the characteristics of faster convergence and higher accuracy.

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历史
  • 收稿日期:2020-03-01
  • 最后修改日期:2020-04-22
  • 录用日期:2020-05-12
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