一种自适应拟牛顿-状态转移混合智能优化算法及应用

针对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,引入局部搜索拟牛顿算子,构造一种混合状态转移算法,以弥补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.混合算法采用自适应调用策略,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.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,验证了混合智能优化算法的有效性,且与其他群智能算法相比,混合算法具有更高的收敛速度与精度.

A hybird state transition optimization algorithm based on adaptive quasi-newton method and its application

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 can 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 can 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.