Multi-objective optimal power flow problem using Nelder–Mead based Prairie Dog optimization algorithm

The Prairie Dog optimization algorithm (PDOA) is a novel metaheuristic algorithm that takes its inspiration from foraging behavior, burrow building activities and communication alarm of Prairie Dog. PDOA has gained extensive popularity among the research community and is now utilized to tackle a variety of optimization challenges. However, the algorithm does not have a healthy equilibrium condition between its exploration and exploitation process. To overcome this problem a novel hybrid algorithm namely Nelder–Mead based Prairie Dog optimization algorithm (NMPDOA) is proposed with the help of Nelder–Mead algorithm (NMA). In this paper, NMPDOA is used for solving the multi-objective optimal power flow (MO-OPF) problems. In order to prove the applicability of the suggested technique, it has been applied to 20 different benchmark functions. The suggested technique is tested in the IEEE 30 and IEEE118 bus standard test system and applied in Indian 62 bus system. To determine the effectiveness of the algorithm, the results of PDOA and NMPDOA are compared with other previously published results discussed in the literature. In addition, PDOA and NMPDOA are also applied in two MO-OPF problems. The statistical analysis and t-test analysis confirm the effectiveness and consistency of NMPDOA for solving real world highly nonlinear and mixed integer optimization problems.