Coverage optimization of wireless sensor network utilizing an improved CS with multi-strategies.

Coverage optimization in wireless sensor networks (WSNs) is critical due to two key challenges: (1) high deployment costs arising from redundant sensor placement to compensate for blind zones, and (2) ineffective coverage caused by uneven node distribution or environmental obstacles. Cuckoo Search (CS), as a type of Swarm Intelligence (SI) algorithm, has garnered significant attention from researchers due to its strong global search capability enabled by the Lévy flight mechanism. This makes it well-suited for solving such complex optimization problems. Based on this, this study proposes an improved Cuckoo Search algorithm with multi-strategies (ICS-MS), motivated by the 'no free lunch' theorem's implication that no single optimization strategy universally dominates. This is achieved by analyzing the standard CS through Markov chain theory, which helps identify areas for enhancement after characterizing the WSN and its coverage issues. Subsequently, the strategies that constitute ICS-MS are individually explained. The evaluation of the proposed ICS-MS is carried out in two phases. First, a numerical comparison is provided, a numerical comparison is presented by contrasting the performance of ICS-MS with the standard CS and its variations employing different strategies in terms of function optimization results. Second, a series of coverage optimization experiments are conducted under various scenarios. The experimental results demonstrate that ICS-MS exhibits significant improvements in both test function optimization and WSN coverage applications. In high-dimensional optimization problems, all enhancement strategies of ICS-MS prove independently effective, showing strong robustness, faster convergence speed, and higher solution accuracy. For WSN coverage optimization, the ICS-MS algorithm outperforms comparative algorithms. At 200 iterations, it achieves an average coverage increase of 2.32-22.17% for 20-node deployments and 2.75-22.21% for 30-node deployments. At 1000 iterations, coverage improves by 1.78-21.65% for 20-node deployments and 1.23-20.99% for 30-node deployments. Additionally, the algorithm demonstrates enhanced stability, more uniform node distribution, and reduced optimization randomness. These improvements collectively elevate coverage rates while lowering deployment costs.