Optimal Power Flow Solution Using Space Transformational Invasive Weed Optimization Algorithm

In this work, a space transformational invasive weed optimization (ST-IWO) algorithm is applied for the solution of single and multi-objective optimal power flow problem. The ST-IWO technique integrates the invasive weed optimization (IWO) and the space transformation search (STS) techniques. The IWO technique is a population-based stochastic algorithm inspired by nature, while the STS is an evolutionary technique inspired by opposition based learning. The STS compares a solution with its opposition to find a better solution which reduces the computational efforts and search direction moves toward the promising region to overcome the premature convergence problem. In order to deal with conflicting objectives of multi-objective problem, the non-interactive approach is applied. In this approach, the decision maker has prior preference information, which eases the selection of the best non-dominated solution. To authenticate the performance of ST-IWO technique, it is tested on standard benchmark functions and three standard IEEE bus systems. The achieved results are compared with recently published results and performance found satisfactory. The statistical test (t test) has been executed to confirm the robustness of the technique.