## 1 Introduction  
A wireless sensor network (WSN) can be defined as a collection of application-specific, low-powered, tiny devices called sensor nodes that are spatially deployed in a geographic area to monitor, collect, process, and cooperatively communicate real-time physical or environmental properties, such as temperature, sound, motion, pressure, humidity, etc. to a central device called a sink using the wireless medium [1]. The advantages of WSN technology, when compared to traditional solutions of networking, are scalability, low costs, accuracy, reliability, flexibility, and deployment ease [2]. This has made WSNs efficient in different application fields, such as military, security, environment, and healthcare. However, the quality of service (QoS) requirements posed by these applications are limited by the resource constraints of the WSN, which are low power, short transmission range, low bandwidth, low memory, and the limited processing and computing speed of the sensor nodes [3]. Basically, a wireless sensor node is a device that consists of a power unit, sensing unit, processing unit, and radio transceiver unit. The majority of the energy consumed in a sensor node is due to data communication with other sensor nodes [4]. The power unit consists of a limited energy source that supplies energy to the other units. Sensor nodes are mostly deployed in harsh environments, which makes sensor battery replacement difficult. Subsequently, as the routing of data packets takes place between the sensor nodes and the sink, the energy gets reduced. Routing means finding the best possible routes from the sensor nodes to the sink. A routing protocol is required between source sensor nodes which also act as routers in WSNs, to find the best paths between source nodes and the sink for reliable communication. Routing protocols are responsible for setting up paths for communication among sensor nodes and the sink [5]. Thus, routing in WSNs is an energy-consuming technique, which makes energy consumption and increasing network lifetime major challenges in WSNs [6]. This implies that if the path followed by a source sensor node to sink is not the best, more energy will be consumed. Energy-efficient routing protocols are expected to distribute the load among sensor nodes to reduce the energy consumption in WSNs and prolong the network lifetime. Route optimization methods, therefore, play a vital role in WSNs, as optimal routing leads to less energy consumption and thus prolongs the network lifetime. Route optimization algorithms in WSNs consider multiple metrics, which include path length, energy, and network lifetime. Thus resulting in a multi-objective optimization problem. Moreover, the dynamically changing topology of WSNs, resulting from sensor nodes stopping activities due to battery expiration from energy consumption, makes route optimization in WSNs an NP-hard problem. This makes routing that uses traditional route optimization techniques based on a deterministic algorithm or Dynamic Programming (DP) such as Dijkstra and Floyd-Warshall not suitable for complex and highly changing conditions of WSNs. This is because of the huge assumptions regarding network condition changes and traffic flows. Artificial intelligence, such as Reinforcement Learning (RL) and Genetic Algorithms (GA), can be applied to find sub-optimal solutions by taking into consideration changing network conditions as they appear in practice [7]. The energy-efficient utilization of sensor nodes can be achieved using three control techniques, which are decentralized control, distributed control, and centralized control [8]. In decentralized control, the nodes are divided into clusters. Each cluster has a central node that coordinates the activity of the nodes in each cluster. The activity of the nodes is therefore determined by the interaction of the central node of each cluster. For distributed control, each node makeslocal decisions with its partial knowledge of the entire network.