Abstract: To address the issues of poor convergence accuracy and susceptibility to local optima in the traditional bald eagle search algorithm when solving complex problems, a multi-mechanism-improved bald eagle search algorithm(FBES) was designed. Firstly, in the phase of selecting the exploration space, a nonlinear weighting factor was integrated with a simplex-based sine-cosine strategy to achieve synergistic optimization of the algorithm’s exploration and exploitation capabilities. Secondly, during the prey diving and capturing phase, the warning mechanism from the sparrow search algorithm was incorporated to enhance the algorithm’s local exploitation ability. Finally, Tent chaotic mapping was applied to the bald eagle population to increase the spatial traversal of solutions and prevent premature convergence. Additionally, comparative experiments were conducted on 11 standard benchmark functions using six algorithms: modified bald eagle search, bald eagle search algorithmbased on chaotic map and adaptive opposition-based learning, self adaptive bald eagle search optimization algorithm, bald eagle search optimization algorithm, dung beetle optimizer and grey wolf optimization algorithm. The experimental data demonstrated that the improved algorithm exhibits significant advantages in convergence efficiency and solution accuracy. Furthermore, the enhanced algorithm was applied to solve engineering problems and wireless sensor network coverage optimization, further validating its practical utility.