A new meta-heuristic programming for multi-objective optimal power flow

In this paper, a new multi-objective approach is suggested, known as multi-objective backtracking search algorithm (MOBSA) in order to formulate and solve the optimal power flow (OPF) problem in power systems. Many objective functions are considered like fuel cost, power losses, and voltage deviation. The structure of the proposed method is simple and has one control parameter. In addition, MOBSA is able to solve the highly constrained objectives. A fuzzy membership technique is integrated into the BSA algorithm to extract the best compromise solution from all the obtained Pareto optimal solutions. Furthermore, the capability of the MOBSA approach is evaluated and verified for bi- and tri-objectives, and tested on three standard IEEE power systems, small network 30-bus, medium network 57-bus, and large network 118-bus test systems. The obtained results reveal that the proposed method is efficient to generate well-distributed Pareto optimal non-dominated solutions. Likewise, the comparison analysis with some re-implemented methods as MODE, SPEA, MALO, and those found in the literature as MOABC/D, QOTLBO, NSGA-II and NSMOGSA, assured the superiority, effectiveness, and robustness of MOBSA.