Application of Clonal Selection Algorithm and Its Variant for Solving Single Objective OPF Problems

Many heuristic optimization techniques derived from evolutionary algorithms are stochastic search methods that mimic the natural biological evolution and/or social behavior of spices. Such algorithms have been developed to obtain near optimal solutions to large scale optimization problems, where in traditional methods may fail. With the development of computational intelligence in recent years, the area of artificial immune systems (AIS) greatly influencing the engineering applications. This paper presents the development and comparative application of recently developed artificial immune system (AIS) based Clonal section algorithm and its variant (adaptive Clonal selection algorithm) for solving single objective optimal power flow (OPF) problems. This problem is formulated as optimization of cost, loss and L-index objectives individually by considering various security constraints. In order to study the effectiveness of the proposed methods, they are tested on standard IEEE 30-bus test system. Based on the comparative results, it is found adaptive Clonal selection algorithm (ACSA) performs better than the basic CSA.