# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 1 Introduction
In fact, clustering is known as an NP-hard problem [22; 23; 27]. However, previous studies show that during cluster head selection with traditional algorithms, it is necessary to employ evolutionary algorithms to increase network efficiency [6; 27; 28; 29; 30]. In this paper, the energy-efficient cluster head selection and routing (ECHSR) method based on evolutionary algorithms such as Particle Swarm Optimization (PSO) and the Harmony Search Algorithm (HSA) are proposed for disaster management. Although PSO has advantages such as being highly dynamic, featuring rapid convergence, and escaping local optimal solutions [23; 27; 29], in the face of high-dimensional optimization, it can be very difficult to search for all the possible solutions in every part of the search space [29; 31; 32]. Having the advantage of a high-search ability, the HSA is capable of solving this problem. Moreover, PSO is confined to exploration and HSA to exploitation, and it is wise to balance exploration and exploitation. Therefore, a hybrid PSO-HSA method will increase energy efficiency more than using these techniques separately [29]. Some studies indicate that meta-heuristic algorithms such as HSA and PSO cannot lead to maximum performance as it is necessary to overcome premature convergence to provide the optimal balance between exploration and exploitation [31; 33; 34]. Therefore, the modified hybrid PSO-HSA method can be a suitable choice for clustering in forest fires. This study is conducted in two main phases: (i) devising an improved hybrid PSO-HSA-based CH selection method, and (ii) developing PSO-based routing with modified tree encoding. In the formulation of the fitness function of the clustering phase, new criteria entitled \"energy efficiency\", \"network closeness\" and \"network coverage\" are considered. In addition to solving fitness function formulas, we use the adaptive weighted sum (AWS) method, which is based on Pareto optimization[28; 35; 36; 37; 38]. Furthermore, for the routing phase, we use the PSO to select relay nodes (RNs) and send packets from CHs to the sink node. However, the PSO may increase the number of invalid paths, which is not suitable for this application [7; 27; 39]. Thus, in order to reduce the invalid paths for our application, we provide an optimal encoding tree based on a new type of packet format. The simulation results demonstrate that, compared to the state-of-the-art, the proposed method increases the network lifetime by expanding the range of network coverage. The contributions of this paper are summarized as follows:  
* Providing an energy-efficient method for clustering and routing in WSNs while considering the characteristics of disaster areas (with a non-uniform distribution of events). * Proposing an improved hybrid PSO-HSA-based CH selection method with a novel fitness function including energy-efficiency, cluster closeness, and network coverage criteria. * Utilizing the PSO to select relay nodes based on multi-hop routing with a fitness function including energy-efficiency and communication link quality criteria. * Using the adaptive weighted sum (AWS) method as a mathematical model to solve the fitness function based on the multi-objective optimization problem (MOP) so that a lower optimization cost can be obtained. * Developing a new type of packet format which is adapted to use in disaster applications; furthermore, modifying the PSO encoding priority-based routing to build an optimal routing tree with non-uniform events. * Overcoming the state-of-the-art approaches in terms of their average energy consumption, the number of live nodes, network coverage, and packet delivery ratio with various sink locations in disaster scenarios. The rest of the paper is organized as follows. A review of the related works is given in Section 2, which is then followed by our proposed method in Section 3. The simulation results and evaluations are described in Section 4. Finally, Section 5 concludes the work and explains the possible future works.