## 1 Introduction
Another interesting routing protocol is the Vice-CH-enabled [16] algorithm, which reduces the overhead provoked by clustering through the consideration of a two-step procedure. In its operation, the CHs are determined to take into account only the nodes that currently survive. The numerical results of Vice-CH-enabled demonstrate that its performance is higher than those based on the LEACH protocol [17]. The LEACH and Vice-CH-enabled protocols are devised to operate with homogeneous WSNs. However, they maintain a low performance when they are applied to HWSNs. Based on the concepts of LEACH and Vice-CH-enabled schemes, other routing protocols [18; 19] were conceived to fulfill the necessities involved by HWSNs. One of the most popular protocols for HWSNs is the Stable Election Protocol (SEP) [20]. In its operation, SEP considers two different node types: advanced and normal elements. In general, advanced nodes present a higher probability than normal elements to become CHs. Therefore, the SEP protocol applies different threshold schemes depending on the node type. The idea behind these techniques is to use the energy of advanced nodes efficiently and avoid the consumption of normal nodes in order to extend the working cycle of the network. Several other protocols have been proposed based on the SEP paradigm. Some examples include the Modified Stable Election Protocol (M-SEP) [21], which evaluates the energy of each node in terms of the complete network to select the nodes that will become CHs. Under such conditions, nodes with higher energy levels present a better opportunity to be CHs than those that maintain low energy values. With the M-SEP method, the life cycle of the network and transmission rate of information is considerably enhanced. Another interesting SEP approach is the P-SEP scheme [22]. The P-SEP evaluates the uncertainty of the node selection though the estimation in each round of an energy threshold. By usingthis energy threshold, it is avoided that a node with a lower energy level than the threshold value turns into a CH. With some concepts similar to P-SEP, the clustering protocol Distributed Energy-Efficient Clustering Algorithm (DEEC) [23] was introduced for HFWSNs. DEEC also uses a threshold limit for selecting the sensor nodes as CHs. Its main characteristic is that the period of time in which a sensor node behaves as a CH depends on the sensor energy level. The idea behind this decision is that high energy nodes produce stable transmission cycles in the network. On the other hand, metaheuristic methods are optimization schemes able to solve complex systems. These techniques are inspired by our scientific understanding of biological or social processes that from an abstraction level, can be considered to be search strategies. Some examples of popular metaheuristic methods include the Particle Swarm Optimization (PSO) [24], Genetic Algorithms [25], the Artificial Bee Colony (ABC) algorithm [26], the Differential Evolution (DE) method [27], the Gravitational Search Algorithm (GSA) [28] and the Flower Pollination Algorithm (FPA) [29]. Metaheuristic schemes do not need convexity, continuity differentiability, or certain initial conditions, which corresponds to an important advantage in comparison to other techniques. Despite their interesting results, these search strategies maintain different difficulties when they are applied to high multi-modal optimization problems. The Yellow Saddle Goatfish Algorithm (YSGA) [30] represents a metaheuristic scheme that emulates the hunting behavior of the Yellow Saddle Goatfish. The algorithm uses different operators that allow a better balance of exploration-exploitation, reducing the typical flaws of other metaheuristic methods such as the premature convergence and the tendency to be trapped in local optima. Such characteristics have motivated their use in different complex engineering optimization problems such as system control [31] and energies [32].