Multi Objective Optimal Power Flow Problems Using Hunger Games Search Algorithm

This paper deal with a multi-objective hunger game search (MOHGS) to solve the optimal power flow (OPF) problem. The traditional HGS is a more advanced optimization technique that bases its decisions on the behavior and actions caused by animal hunger. The selected optimization method has been used to solve various multi-objective functions. The fuel cost, emissions, and voltage deviation are included in these objective functions. The suggested technique has an easy structure with few control parameters. MOHGS can also deal with objectives that are highly constrained. The proposed approach uses a fuzzy membership technique to determine the best compromise solution from all the produced Pareto optimal set. The MOHGS algorithm incorporates a crowding distance technique to rank and arrange Pareto optimal solutions. The MOHGS approach's aptitude for Bi- and Tri-objectives is assessed, verified, and tested on an IEEE 57-bus power system with four case studies. The simulation results demonstrate the effectiveness of the suggested strategy in producing well-distributed Pareto optimal solutions.