融合能量周期性递减与牛顿局部增强的改进HHO算法

为增强栗翅鹰优化算法的全局探索能力和局部开采性能,提出一种融合能量周期性递减机制与牛顿局部增强策略的改进栗翅鹰优化算法(improved harris hawks optimization,IHHO).该算法在传统HHO算法基础上,启发于自然界中鹰与猎物间的多轮围捕-逃逸现象且猎物能量整体上呈现递减态势,进而设计一种猎...

Improved harris hawks optimization coupling energy cycle decline mechanism and Newton local enhancement strategy

To strengthen global exploration and local exploitation capacity of harris hawks optimization(HHO), an improved HHO(IHHO)algorithm is proposed by coupling the energy cycle decline mechanism and the Newton local enhancement strategy. On the basis of the canonical HHO algorithm, a control coefficient of the prey energy cycle decline mechanism is designed and absorbed into the original prey energy function, which inspires multi-round besiege- escaping phenomena between hanks and prey in nature. On the whole, the prey energy is declining with iterations. This mechanism contributes to dynamically balancing the global and local searching ability of HHO. Meanwhile, in consideration of better property of Newton iteration thought, a kind of Newton local reinforcement strategy is constructed for re-exploiting the local neighbourhood of prey(current optima), which results in improving the local searching performance of the IHHO algorithm with probability. Experimental results show the performance difference influence of different number of cycles and local searching on HHO, the superior parallel iterative optimization ability and convergence accuracy of the proposed algorithm, and its better applicability on high dimension cases(100D-10000D).