群体智能优化算法与传统算法相比，具有易于实现，稳定性强，自组织性强，并行性好的优点，因此广泛应用于工程领域的优化问题。作为群体智能优化算法范畴的麻雀搜索算法是薛建凯在2020年首次提出的一种新兴的智能优化算法，算法受麻雀寻找食物和躲避捕食者的生物学行为启发，与其他群体智能优化算法相比，麻雀搜索算法具有独特的搜索模型和优化能力，更好的全局搜索能力，搜索结果具备更强的稳定性。此外，通过研究发现麻雀搜索算法并不适合求解离散型的优化问题，在搜索的后期由于种群多样性的减少，会出现陷入局部最优的情况。针对麻雀搜索算法存在的问题，本文以提出一个具有更优搜索能力的麻雀搜索算法为研究目标，主要工作内容如下：

首先提出一种多策略改进的自适应麻雀搜索算法，使用 Iterative 混沌映射初始化种群，使得种群在整个搜索空间内分布更加均匀，丰富了种群的多样性；自适应调整领导者和跟随者比例系数，有效地平衡全局搜索和局部搜索；改进领导者位置更新公式；采用非线性正余弦搜索算法改进跟随者位置，最后通过柯西变异和 Tent 混沌扰动对个体进行调整，避免种群陷入局部最优的情况。其次，为验证改进后算法的性能，进行三组测试实验，分别选取7个单峰测试函数、4个多峰测试函数、6个固定维度测试函数，并将多策略改进的自适应麻雀搜索算法与麻雀搜索算法、灰狼优化算法、粒子群算法、蝴蝶优化算法进行对比实验，结果表明改进后的麻雀搜索算法收敛速度更快，收敛精度更高。最后，将其应用于压力容器优化设计问题、移动机器人路径规划问题与无线传感器网络覆盖优化问题。在压力容器设计优化问题中，改进后的麻雀搜索算法的结果由于其他四种算法，可以有效减少总费用。在移动机器人路径规划问题中，改进后的麻雀搜索算法规划的路径长度与效果优于其他四种算法。在无线传感器网络覆盖优化中，二维平面和三维空间的仿真结果表明，与未改进的麻雀搜索算法及随机覆盖的无线传感器网络相比，改进后的麻雀搜索算法优化了节点分布，提高了无线传感器网络节点模型的覆盖率，验证了算法的实用性和可行性。

本文主要针对麻雀搜索算法存在的一些问题，对其进行了分析和改进；将改进后的算法在测试函数上进行了仿真实验，仿真结果表明：该算法具有更佳的性能；此外，将其应用于实际工程问题，实验结果表明：改进后的算法具有一定的可行性和有效性。

Compared with traditional algorithms, swarm intelligence optimization algorithm has the advantages of easy realization, strong stability, strong self-organization and good parallelism, so it is widely used in optimization problems in engineering field. As the category of swarm intelligence optimization algorithm, sparrow search algorithm is an emerging intelligent optimization algorithm first proposed by Xue Jiankai in 2020. The algorithm is inspired by the biological behavior of the sparrow foraging and avoiding predators. Compared with other swarm intelligence optimization algorithms, the sparrow search algorithm has a unique search model and optimization ability, better global search ability, and more stable search results. In addition, it is found that the sparrow search algorithm is not suitable for solving discrete optimization problems, and the population diversity decreases in the later stage of the search, and it is easy to fall into the local optimum. Aiming at the problems of sparrow search algorithm, this paper aims to propose a sparrow search algorithm with better search ability. The main work is as follows:

Firstly, an improved multi-strategy adaptive sparrow search algorithm is proposed. Iterative chaotic map is used to initialize the population, making the population distribution more uniform and increasing the diversity of the population. The proportion coefficient of discoverer and follower is adjusted adaptively to balance the global search and the local search. Improve the discoverer position update formula; A nonlinear sine-cosine search algorithm is used to improve the follower position. Finally, individuals are adjusted by Cauchy mutation and Tent chaos perturbation to avoid the local optimal situation. Secondly, in order to verify the performance of the improved algorithm, three groups of test experiments are carried out. Seven unimodal test functions, four multimodal test functions, Six fixed dimension test functions, and the multi-strategy improved adaptive sparrow search algorithm is compared with the sparrow search algorithm, gray wolf optimization algorithm, particle swarm optimization algorithm, butterfly optimization algorithm. The results show that the improved sparrow search algorithm has faster convergence speed and higher convergence accuracy. Finally, it is applied to pressure vessel optimization design, mobile robot path planning and wireless sensor network coverage optimization. In the pressure vessel design optimization problem, the improved sparrow search algorithm can effectively reduce the total cost because of the other four algorithms. In the path planning problem of mobile robot, the improved sparrow search algorithm has better path length and effect than the other four algorithms. In wireless sensor network coverage optimization, two dimensional plane and three dimensional space simulation results show that Compared with the unimproved sparrow search algorithm and random coverage of wireless sensor networks, the improved sparrow search algorithm optimizes the distribution of nodes, improves the coverage of wireless sensor network node models, and verifies the practicality and feasibility of the algorithm.

This paper analyzes and improves the sparrow search algorithm in view of its shortcomings. The improved algorithm is simulated on the test function. The simulation results show that the algorithm has better performance. In addition, it is applied to practical engineering problems, and the experimental results show that the improved algorithm is feasible and effective.