原有的黑寡妇优化算法框架简单，参数设置直观，适用于多种优化问题，能够有效地探索解空间，具备强大的全局搜索能力。然而在某些情况下，该算法可能会过早收敛到局部最优解，还可能需要较长的时间。针对黑寡妇优化算法存在的问题，本文提出基于信息素挥发-追踪机制的改进黑寡妇优化算法，并进行了仿真实验研究。

首先，Logistic-Tent 映射作为初始化策略，该方法能在初始化阶段生成更多样化的个体，不仅提升了种群的初始质量，还增强了算法整体的优化效率；引入了信息素挥发-追踪机制，不仅增强了探索与利用的平衡，而且提高了在复杂搜索空间中避免局部最优和加速收敛到全局最优的能力；采用了 Sine 映射和柯西分布变异，不仅增强了黑寡妇算法的全局搜索能力，还提高了解的多样性和算法的稳定性。

其次，为验证改进后算法的性能，选取 23 个测试函数进行实验，将改进的黑寡妇优化算法与原黑寡妇优化算法、灰狼优化算法、蝙蝠优化算法、蜘蛛蜂优化算法以及萤火虫优化算法进行对比。对比结果表明：改进后的算法能够有效提升算法的收敛精度和鲁棒性。

最后，将改进算法应用于优化设计问题，分别以二维栅格路径问题和无人机(UAV)三维路径规划问题为例。在二维栅格设计优化问题中，设置了 20×20 与 40×40 两组二维栅格地图，改进后的黑寡妇优化算法规划结果最优，路径长度最短；在无人机(UAV)三维路径规划问题中，通过建立无人机三维路径模型证明了改进后的黑寡妇优化算法的最优路径同时满足了安全性（如避障）和可行性（如飞行高度、转弯率限制），实现了多目 标综合成本最小化，验证了算法的实用性和可行性。

在文章的结尾，总结了本文的研究成果，阐述了当前算法的不足之处和未来探索方向。本文借助对黑寡妇优化算法的改进和应用，使得群优化算法在工程领域实现了更广泛的应用。

The original Black Widow optimization algorithm has a simple framework and intuitive parameter settings, making it suitable for various optimization problems. It can effectively explore the solution space and possesses strong global search capabilities. However, in certain situations, this algorithm may converge too early to local optima and may require longer computation times.To address the issues with the Black Widow optimization algorithm, this paper proposes an improved black widow optimization algorithm based on information pheromone evaporation-tracking mechanism and conducts simulation experiments.

First, logistic-tent mapping as an initialization strategy can generate more diverse individuals during the initial stage, not only improving the initial quality of the population but also enhancing the overall optimization efficiency of the algorithm; it introduces a pheromone volatilization-tracing mechanism, which not only enhances the balance between exploration and exploitation but also improves the ability to avoid local optima and accelerate convergence to global optimality in complex search spaces; the use of sine mapping and Cauchy distribution variation not only strengthens the global search capability of the Black Widow algorithm but also increases the diversity of solutions and the stability of the algorithm .

Secondly, to verify the performance of the improved algorithm, 23 test functions were selected for experimentation. The improved Black Widow optimization algorithm was compared  with the original Black Widow optimization algorithm, Grey Wolf optimization algorithm, Batoptimization algorithm, Spider Bee optimization algorithm, and Firefly optimization algorithm. The results show that the improved algorithm can effectively enhance the convergence accuracy and robustness of the algorithm.

Finally,the improved algorithm was applied to optimization design problems, using two dimensional grid path problems and three-dimensional path planning for unmanned aerial vehicles (uav) as examples. In the two-dimensional grid design optimization problem, two sets of 20x20 and 40x40 two-dimensional grid maps were set up, and the improved Black Widow optimization algorithm yielded the best results, with the shortest path length. In the three-dimensional path planning for uav, a three-dimensional path model of the uav was established to demonstrate that the improved Black Widow optimization algorithm's optimal path simultaneously meets safety requirements (such as obstacle avoidance) and feasibility (such as flight altitude and turn rate constraints), achieving multi-objective comprehensive cost minimization, thus verifying the practicality and feasibility of the algorithm.

At the end of this paper, the research results are summarized, and the shortcomings of the current algorithm and the future exploration direction are expounded. With the improvement and application of the black widow optimization algorithm, the swarm optimization algorithm has been applied more widely in the engineering field.

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