Aiming at the drawbacks that Sparrow Search Algorithm （SSA） has relatively low search accuracy and is easy to fall into the local optimum， an Enhanced Sparrow Search Algorithm based on Multiple Improvement strategies （EMISSA） was proposed. Firstly， in order to balance the global search and local search abilities of the algorithm， fuzzy logic was introduced to adjust the scale of sparrow finders dynamically. Secondly， the mixed differential mutation operation was performed on sparrow followers to generate mutation subgroups， thereby enhancing the ability of EMISSA to jump out of the local optimum. Finally， Topological Opposition-Based Learning （TOBL） was used to obtain topological opposition solutions of sparrow finders， thereby fully mining high-quality position information in the search space. EMISSA， standard SSA and Chaotic Sparrow Search Optimization Algorithm （CSSOA） were evaluated by 12 test functions in 2013 Congress on Evolutionary Computation （CEC2013）. Experimental results show that EMISSA achieves 11 first places on 12 test functions in the 30-dimensional case； in the 80-dimensional case， the proposed algorithm has the optimal results on all the test functions. In the Friedman test， EMISSA ranks first on all the test functions. Experimental results of applying EMISSA to the Wireless Sensor Network （WSN） node deployment in obstacle environment show that compared with other algorithms， EMISSA achieves the highest wireless node coverage with more uniform node distribution and less coverage redundancy.

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.