The study of the behavior of social organisms in the search for food is the cornerstone for the development of modern example algorithms. With this in mind, an algorithm of spider monkey examples appeared that mimics the flexible social structure of these monkeys. The algorithm is mainly based on the principles of self-organization and task distribution, which are the pillars of swarm intelligence. Thanks to its high efficiency, today it has become a popular option in addressing various engineering problems. This review deals with a detailed explanation of the algorithm, enhanced by a practical example (digital) for easy understanding and assimilation. To solve the issue of optimizing the optimal power consumption using the spider monkey algorithm and its chaotic development using Chaos Theory (chaos maps) in the MATLAB program, with the aim of reducing power consumption and increasing the speed of convergence in the wireless network, where the" monkey locations " represent the variables that affect the power (such as reducing distances, optimizing the path and load distribution ) the most important conclusions were to help the developed program not only optimize the distance, but also carry out a process of "dynamic tuning" of the search space. The mathematical results confirm that the integration of Chaos Theory transformed the SMO algorithm from a " random researcher "to an" intelligent researcher " with a wider spatial memory, which reduces the energy wasted on long exploration tours. The study proved that the integration of a chaotic logistics map with the spider monkey algorithm leads to a longer Network life: by reducing the average transmission distance., System stability: reduce the fluctuation in the choice of Group headers, and reach the optimal solution in about half the time.