Objective Wireless sensor networks (WSNs) are essential for ocean monitoring and are widely used in environmental monitoring, target localization, marine resource development, and disaster warning applications. However, WSNs often face challenges such as arbitrary deployment strategies, low effective coverage, and high coverage redundancy, which degrade network performance. To address these issues, this paper proposes a fractional-order chameleon swarm algorithm (FCSA) to optimize the deployment of static WSN nodes.

Method First, an improved Circle chaotic mapping method is employed to enhance population diversity and global distribution, ensuring higher-quality initial conditions for optimization. Next, during the velocity update phase, a fractional-order velocity update strategy is introduced to effectively leverage the historical search experiences of individuals, enhancing the balance between global exploration and local exploitation. Furthermore, the Levy flight mechanism is incorporated into position updates, providing stronger jumping characteristics and adaptability. These improvements enable FCSA to effectively optimize key performance indicators such as coverage rate and coverage redundancy, significantly enhancing deployment efficiency and distribution uniformity for static WSN nodes while ensuring better adaptability to complex environments.

Results Simulation results demonstrate that FCSA outperforms CSA, CSA-Circle, CSA-Levy, GA, RSO, and eleven other classical optimization algorithms in static node deployment. FCSA achieves a high coverage rate of 0.801 8 while significantly reducing coverage redundancy to 0.007 8. Additionally, for single optimization tasks, FCSA exhibits the fastest convergence, requiring only 638 iterations to reach a fitness value of 0.191 409, significantly outperforming other algorithms. After 30 independent runs, statistical analysis shows that FCSA maintains an extremely fast convergence speed in the early iterations, reaching an optimal fitness value of 0.198 222 after 1 000 iterations. Among the ten algorithms, FCSA is the only one with a standard deviation of the fitness value below 0.2, indicating superior global search ability, higher convergence accuracy, and better distribution uniformity. It effectively mitigates the issue of uneven node distribution observed in traditional algorithms while maintaining strong stability and robustness.

Conclusion In addressing the WSN static node deployment problem, FCSA effectively optimizes sensor placement, significantly improving monitoring quality through a multi-strategy collaborative optimization approach. The algorithm exhibits strong robustness and adaptability in complex environments. Additionally, FCSA provides an efficient, high-quality deployment solution for ocean monitoring and similar applications, offering strong theoretical and technical support for sensor network optimization and expansion, with significant application potential and practical value.