M.A. Thesis:

sine cosine Based Energy-Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks

Nature-inspired optimization algorithms as smart optimization methods along with classical methods have shown considerable file success and are used in solving many optimization problems in various fields such as engineering, industry, trade, etc. have became. In this paper, a new evolutionary algorithm based on sine and cosine wave behavior is sought to seek to optimize wireless sensor networks and increase lifespan and reduce energy. In this article, the goal is to improve the convergence problem while maintaining the accuracy of the appropriate answer. The proposed solution in this paper is using fuzzy inference system and chaos mapping and the results show that the rate of improvement in algorithm convergence or reduction in the number of optimal function evaluations has occurred and this improvement was generally more than 28% in twelve test functions. This is while in terms of obtaining the answer to the problem in the proposed method no reduction is observed.

Keywords: Optimization, cosine sine algorithm, wireless sensor network, metaheuristic algorithm, fuzzy inferen