Table 1 Summary of literature review.

¹⁵

Honey Badger algorithm

Enhanced coverage through parameter optimization, improving balance between exploration and exploitation.

Requires fine-tuning of parameters for optimal performance.

¹⁶

Genetic algorithm with sensor clouds

Integrates genetic algorithm for clustering and path planning, improving efficiency and coverage.

High computational cost and complex setup required.

¹⁷

Ant lion optimizer with NSGA-II

Focuses on network coverage and minimizing sensor node movement, integrating NSGA-II for multi-objective optimization.

May converge to local optima without extensive parameter tuning.

¹⁸

Particle swarm optimization (PSO)

Optimizes sensor deployment locations through PSO, addressing minimum exposure path issues.

PSO can be trapped in local optima in complex environments.

¹⁹

Improved Sparrow Search Algorithm

Addresses 3D WSN coverage with enhancements like safety threshold attenuation and stagnation update procedures.

Specific to 3D environments, not generally applicable.

²⁰

Army ant search optimizer

Enhances network coverage and lifetime with a multi-objective approach, integrating fast-nondominated sorting and chaotic mapping.

Increased computational complexity with multiple optimization layers.

²¹

Harris Hawk optimization

Utilizes HHO for optimal router placement for maximum connectivity and coverage.

Performance varies significantly with network size and complexity.

²²

Self-adaptive artificial bee colony

Improves node deployment with a strategic pool and adaptive selection mechanism to enhance performance.

Requires constant adaptation to maintain efficiency.

²³

NSGA-III

Solves multi-objective optimization problems to maximize connectivity and coverage while minimizing energy consumption.

Complex and resource-intensive, challenging to implement in larger networks.

²⁴

Cuckoo search algorithm (Enhanced)

Improves WSN performance through optimized non-uniform clustering and faster convergence with Cauchy distribution.

Potential for premature convergence if not properly tuned.

²⁵

Whale optimization with Levy flight

Enhances exploration capabilities of the whale optimization algorithm to avoid local optima and improve coverage efficiency.

Specific tuning required to balance exploration and exploitation.

²⁶

Voronoi-Glowworm swarm optimization-K-means

Combines multiple algorithms to optimize the sensing radius and enhance network lifetime through effective node deployment.

Complex integration of multiple algorithms can lead to operational inefficiencies.

²⁷

PSO and chaos optimization

Encodes nodes as particle positions to move towards optimal locations, enhancing coverage with chaos optimization.

Requires careful calibration to ensure effective optimization without overspending energy.

²⁸

Vampire bat algorithm with bipartite graph

Optimizes node moving distance during redeployment, integrating virtual force-based optimization for refined coverage.

Dependent on the initial deployment strategy for effectiveness.

²⁹

Mult objective Dingo optimization

Focuses on dynamic energy decay and adaptive coverage strategies, optimizing node sensing capabilities over time.

Specific to applications requiring high adaptability in sensing and energy management.