Selection the optimum cluster head in the wireless sensor networks and adaptive clustering via optimization algorithms

This thesis is a study on the solution of the problem of capacity servers' location in sensor networks. This problem is part of the group of np hard combinatorial problems. With the solution of the problem, both adaptive clustering and optimal cluster head selection have been achieved. In this study, cluster head selection is carried out by optimization techniques such as particle swarm intelligence optimization, differential evolution algorithm, and imperialist competitive algorithm. The purpose of each iteration is dependent upon the value of the function and clustering and network configuration change accordingly. Tests results show that ICA Algorithm has better results in terms of speed, while PSO Algorithm is better in terms of costs. Since processors with limited capacity are used in wireless sensor networks, it is necessary to use the most appropriate algorithm. ICE algorithm has good performance, and as it uses low processing power, it is suggested for use in wireless sensor networks. Key Words: Wireless Sensor Networks, Optimization, Adaptive Clustering, Artificial Intelligence, Particle Swarm Optimization, And Imperialist Competitive Algorithm, Differential Evolution Algorithm