In recent years, wireless sensor networks (WSNs) have been growing rapidly because of their ability to sense data, communicate wirelessly, and compute data efficiently. These networks contain small and low-powered sensor nodes that organize and configure themselves to carry out their functions. Even though WSNs are cheap, easy to deploy, flexible, and efficient, there are some challenges in terms of energy efficiency and network lifetime. Clustering in WSNs is the most reliable solution for the challenges, in which nodes are grouped into few clusters, and a cluster head (CH) is selected for data aggregation and data transfer to the base station (BS). However, there are still many challenges such as energy hole and isolated node problems that exist because of inefficient CH selection and cluster formation methods. In this work, we comprehensively reviewed various nonmetaheuristic and metaheuristic methods for CH selection and cluster formation that are used in networks from various environmental settings, for a better understanding of how the aforementioned problems are tackled by some authors. Moreover, the methods’ parameter settings, advantages, limitations, and future directions are presented with a brief performance summary of the approaches.

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.