Some of the best optimization solutions were inspired by nature. Clonal selection algorithm is a technique that was inspired from genetic behavior of the immune system, and is widely implemented in the scientific and engineering fields. The clonal selection algorithm is a population-based search algorithm describing the immune response to antibodies by generating more cells that identify these antibodies, increasing its affinity when subjected to some internal process. In this paper, we have implemented the artificial immune network using the clonal selection principle within the optimal lab system. The basic working of the algorithm is to consider the individuals of the populations in a network cell and to calculate fitness of each cell, i.e., to evaluate all the cells against the optimization function then cloning cells accordingly. The algorithm is evaluated to check the efficiency and performance using few standard benchmark functions such as Alpine, Ackley, Rastrigin, Schaffer, and Sphere.