Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

The optimal power flow (OPF) is a practical problem in system with complex characteristics such as large number of control parameters and also multi-modal non-convex objective functions inequality nonlinear constraints. Thus, tackling the OPF becoming major priority for engineers researchers. Many metaheuristic algorithms different search strategies have been developed to solve problem. Although, majority them suffer from stagnation, premature convergence, local optima trapping during optimization process, which results producing low solution qualities, especially real-world problems. This study devoted proposing an effective hybridizing whale algorithm (WOA) modified moth-flame (MFO) named WMFO In proposed WMFO, WOA MFO cooperate effectively discover promising areas provide high-quality solutions. A randomized boundary handling used return solutions that violated permissible boundaries space. Moreover, greedy selection operator defined assess acceptance criteria new Ultimately, performance scrutinized on single multi-objective cases problems including standard IEEE 14-bus, 30-bus, 39-bus, 57-bus, IEEE118-bus test systems. obtained corroborate outperforms contender solving