Wireless sensor networks encounter issues like irregular deployment, node failures, and uneven energy consumption that create coverage holes, leading to a reduction in network lifetime in critical or disaster-based applications. Most existing approaches focus on coverage enhancement during the initial deployment and perform mitigation only at the beginning of the network operation. However, the coverage holes may also occur later due to node failures and energy depletion. To address this issue, a Hybrid Raccoon–Hermit crab optimization algorithm that advocates both initial coverage enhancement and adaptive mitigation due to future coverage holes is proposed. The proposed algorithm uses the global exploration ability of the raccoon optimization algorithm to find optimal cluster heads and the exploitation ability of the Hermit crab optimization to determine the optimal position and to relocate the static nodes logically to mitigate coverage holes. The proposed algorithm is evaluated under different node densities (50, 100, 200, 500, and 1000), with the sink at $(100,100)$. It results in an enhanced network lifetime of $65.20\%$, an improved coverage ratio $(16.94\%)$ from $(77.05\%)$ to $(93.94\%)$, increased throughput by delivering (3,139,293) bits, and a reduced delay of $2.27292$ s for 1000 nodes compared with other existing methods.