الفهرس 
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Abstract
In this thesis, a comparative study has been done for different clustering algorithm under different network density and different location for BS. Then, a proposed a suite of Evolutionary Algorithms (EA)-based protocols to solve the problems of clustering in Wireless Sensor Networks (WSNs). At the beginning, the problem of the Cluster Heads (CHs) selection in WSNs is formulated as a single-objective optimization problem. A centralized weighted-sum multi-objective optimization protocol is proposed to find the optimal set of CHs. The proposed protocol finds a predetermined number of CHs in such way that they form one-hop clusters. The goal of the proposed protocol is to enhance the network’s energy efficiency and the protocol’s scalability. The formulated problem has been solved using two evolutionary approaches: Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA).Then, to maximizes the protocol’s scalability using two-hop communication between the CHs using proposed Cost function Then, a centralized weighted-sum PSO-GSA protocol is proposed for finding the optimal set of CHs in underwater WSN (UWSN). Finally, the heterogeneity has been considered to simulate a realistic environment, in this thesis the heterogeneous energy and traffic is considered and new protocol has been proposed to offload heavy energy consumption in CHs. Extensive study for the following points are considered in details: (1) Comparative study for recently clustering algorithm under different network size and different location for BS. (2) study and analyze the performance of clustered WSN using GSA and PSO optimization algorithms. (3) hybrid PSO-GSA algorithm has been used to solve the problem of finding optimal set of CHs. (4) Routing tree has been constructed between CHs using a proposed Cost function to make the algorithm more realistic for large scaled area. (4) study the performance of clustered Underwater WSN in 3-Dimentional model using Hybrid PSO-GSA algorithm. (5) introduce the heterogeneity in energy and traffic in the network and select near optimal sensor node to offload energy consumption in CHs.