## 3 Related Work
One algorithm iterates repeatedly and updates the swarming behavior. To discover the global optimum or optimal solution, a fish simulation behavior is done through a randomized parallel algorithm. [PERSON] [23] proposed an improved Glow-worm swarm optimization (IGSO) algorithm to enhance the performance of multi-dimensional problems and convergence rate. A population of glowworms with an identical quantity of luciferin is dispersed across the search space. The value of luciferin is determined by the glowworm's location. Brighter light in the field indicates more luciferin. To update the position of the glow-worm, a randomized algorithm based on parameter adaptation is used which is followed by the luciferin update. [PERSON] [24] proposed a Harmony Search Algorithm (HSA) PSO-based hybrid algorithm to overcome the different local search constraints and exploration-exploitation trade-offs. This hybrid scenario also obtained a fast convergence rate in global search and has improved the lifetime of sensor nodes due to its dynamic capability and high search efficiency. [PERSON] [25] presented a GA-PSO hybrid approach that explores the distributed clustering levels for large-scale WSNs. At the lower level, GA is used in independent subgroups for a global search, and, at the upper level, the PSO algorithm is used for the local search of individuals. The proposed technique also reduces energy consumption and accelerates convergence speed. [PERSON] [26] proposed an improved protocol of LEACH which is inspired by Low-energy adaptive clustering with the collaboration of GA and Bacteria Foraging (BF) implementation to overcome the disadvantages of former conventional protocols. The proposed algorithms also reduce energy dissipation and improve the network's lifetime. [PERSON] [27] presented a new hybrid approach by combining GA, PSO, and Symbiotic Organisms Search (SOS) based on the natural selection phenomenon. GA creates and picks the best population for the future phases, PSO accumulates and updates experience for each appropriate solution, and SOS builds on prior phases and executes symbiotic interaction update phases in the real-world population. [PERSON] [28] proposed DE-SA hybrid approach for best cluster head selection. It is utilized to extend the life of the network by delaying the death of cluster heads. The cluster head's remaining energy and the distance between the nodes are taken into account by the fitness function concerning the differential evolution and simulated annealing concept (DESA). In this technique, the authors have tried to keep the maximum no. of sensor nodes alive, as the network's lifetime is directly proportional to the number of nodes alive. [PERSON] [29] proposed Firefly Algorithm with Particle Swarm Optimization (HFAPSO) which is provided in the LEACH-C approach for selecting the optimal cluster head selection. The hybrid method optimizes firefly global search behavior and achieves optimal cluster head positioning using PSO. The suggested methodology's performance is measured by the number of live nodes, available energy, and performance. [PERSON] [30] proposed a Hybrid GWO-based Sunflower optimization (HGWSFO) technique for optimum CHS under particular factor constraints such as energy consumption and separation distance, to extend network longevity. Balancing the exploration-exploitation trade-off increases network performance in terms of total throughput, node residual energy, dead nodes, alive nodes, network survivability index, and convergence rate. Table 1 provides a comprehensive comparison of algorithms based on their strengths, drawbacks, performance influencing variables, and application areas.