## 1 Introduction  
Wireless Sensor Networks (WSN) can detect, store, and transmit data in real-time. These tasks must be completed efficiently to avoid wasting the limited sensor battery life. We cannot extend the sensor's life by providing external or extra energy since most sensors are placed in difficult-to-reach locations. With a lot of work, the sensor node's lifespan has been prolonged. In addition to the energy limitation, WSNs confront a variety of problems, including precise sensing and non-redundant information. There are three types of WSN significant issues: energy efficiency, security, and service quality (QoS). Many of these concerns are subject to trade-offs such as network lifetime for a better QoS. The same is true for the security parameters. Individually solving these problems has taken a considerable amount of time and effort. When dealing with these problems separately, there are several flaws. As a result, to create better WSNs, we must address all of these problems at the same time. On the other hand, meta-heuristics methods are problem-independent. They can be utilized as a black box since they are non-adaptive and non-greedy. These algorithms frequently allow temporary deterioration of the solution to reach the global optima. Meta-heuristic or intelligent optimization algorithms are sometimes known as nature-inspired algorithms. The natural environment serves as inspiration for these algorithms. There are four types of nature-inspired/meta-heuristic algorithms: bio-inspired, physics-inspired, geography-inspired, and human-inspired. Biological systems are the source of the great majority of nature-inspired algorithms. As a result, bio-inspired algorithms (biology-inspired) comprise a large portion of nature-inspired algorithms as shown in Figure 1.  
The goal of the optimization process is to discover the best solution to a given issue. The selection of an appropriate algorithm is critical for achieving this goal. However, certain issues are complicated, and finding all feasible solutions is challenging. Several meta-heuristic algorithms have been created in the literature to simulate the biological behavior of animal or insect groups by creating deterministic or random rules to be used in addressing various optimization issues. Nature-inspired hybrid algorithms are designed to overcome different constraints in WSNs. Many researchers have implemented different meta-heuristic algorithms in the past to improve the lifetime, stability, and performance of the entire WSN. Hybridization techniques in optimization algorithms have helped in improving the network lifetime, stability period, throughput, number of dead nodes per iteration, and residual energy of the network. Sometimes, these bio-inspired algorithms evaluate incorrect solutions for some real-time applications. Convergence speed, multiple objective problems, dynamic problems, and local optima convergence are hot research problems nowadays. Hybridization of algorithms requires a large number of functions to be evaluated, resulting in more accuracy and improved performance of WSNs. Researchers have suggested the use of creating and optimizing a multi-objective function with a suitable mathematical function-based optimizer or hybridization technique to solve challenging, dynamic, and multi-objective problems in WSN. This paper mainly focuses on how different hybrid metaheuristic approaches play an important role in enhancing the overall performance of WSNs and their comparative analysis followed by contributions given by researchers in this field. We also discuss and compare various techniques to choose the cluster head. The problems, open  
Figure 1: Classification of Nature-inspired Optimization Algorithm. issues, and challenges faced in Bio-inspired optimization techniques have been elaborated with various solutions followed by concluding remarks. This paper has discussed the three main types of bio-inspired algorithms: evolutionary, swarm-based, and plant-based optimization.