To improve the performance of Yin-Yang-Pair Optimization-Simulated Annealing1 （YYPO-SA1）， a Yin-Yang-pair optimization algorithm based on dynamic D-way splitting and chaotic perturbation NYYPO （Newton-Yin-Yang-Pair Optimization） was proposed. Firstly， in order to dynamically adjust the probability of D-way splitting， Newton’s law of cooling mechanism was adopted. Then， the chaotic perturbation strategy was applied in splitting stage. The dynamic adjustment mechanism was applied to enable NYYPO to use a larger D-way segmentation probability at the early stage of search， and use a smaller D-way segmentation probability at the late stage of search， which enhanced the global search ability of the algorithm. Meanwhile， the diversity of solution was enriched， and the ability of the algorithm to jump out of local optimum was improved by using chaotic perturbation strategy. Finally， NYYPO was applied to the parameter optimization design problem of wind-driven generator. Fifteen test functions， including unimodal， multimodal， and composite functions， were selected to evaluate the performance of NYYPO， YYPO-SA1， and 6 representative single-objective optimization algorithms： Particle Swarm Optimization （PSO） algorithm， Crow Search Algorithm （CSA）， Grey Wolf Optimizer （GWO）， Whale Optimization Algorithm （WOA）， Flower Pollination Algorithm （FPA）， and Sparrow Search Algorithm （SSA）. The results show that compared with YYPO-SA1， NYYPO obtains 12 orders of magnitude improvement on Sphere function. In Friedman test， when dimension is 10， 30， 50 respectively， NYYPO ranks 2.87， 2.0 and 1.93 averagely and respectively， total ranking of all of them is the first. It can be seen that NYYPO achieves significant performance advantages in statistical significance. At the same time， NYYPO also achieves better optimization results in the parameter optimization design problem of wind-driven generator.