Adaptive Hybrid Harris Hawks Optimization with Differential Evolution for Maximizing Network Lifetime under MDCCKC Constraints in Heterogeneous Wireless Sensor Networks

Heterogeneous Wireless Sensor Networks (HWSNs) are widely used in surveillance, environmental monitoring, and industrial sensing applications due to their flexible deployment and distributed nature. However, limited battery power and uneven energy utilization among sensor nodes significantly reduce network lifetime, especially in target-based coverage networks where predefined targets must be continuously monitored. Ensuring reliable sensing through k-coverage while maintaining end-to-end connectivity to the sink remains a challenging task. In addition, maximizing the number of Maximum Disjoint Connected Covers under k-coverage constraints (MDCCKC) is an NP-hard combinatorial optimization problem. To address these issues, this paper proposes an Adaptive Hybrid Harris Hawks Optimization with Differential Evolution (AHHO-DE) algorithm for maximizing network lifetime in HWSNs under MDCCKC constraints. The proposed method integrates the strong exploration ability of Harris Hawks Optimization (HHO) with the exploitation and solution refinement capability of Differential Evolution (DE). A stagnation-aware adaptive mechanism is introduced in which DE operators are dynamically triggered during the HHO process to avoid premature convergence and enhance convergence stability. A multi-constraint fitness function is designed by incorporating target coverage, k-coverage satisfaction, connectivity enforcement, transmission distance cost, and residual energy consumption to generate feasible and energy-efficient disjoint connected cover sets. MATLAB simulations are conducted in a 100 m × 100 m area with randomly deployed heterogeneous nodes and 25 targets. Performance is evaluated using network lifetime, success ratio, energy consumption, packet delivery ratio (PDR), and packet loss ratio (PLR). Results show that the proposed AHHO-DE-MDCCKC method significantly outperforms EDTC, ACO-MNCC, BFO-MDCCKC, and GA-based approaches. For 100 nodes, the proposed method achieves 368 rounds of lifetime, 97% success ratio, and only 3% packet loss ratio with lower energy consumption. The results confirm that AHHO-DE provides an effective and scalable solution for lifetime maximization in heterogeneous target coverage wireless sensor networks.