Migration-Based Moth-Flame Optimization Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various tasks. However, MFO cannot provide competitive results for complex problems. The sinks into the local optimum due to rapid dropping of population diversity and poor exploration. Hence, this article, migration-based moth–flame (M-MFO) proposed address mentioned issues. In M-MFO, main focus on improving position unlucky moths by migrating them stochastically early iterations using random migration (RM) operator, maintaining solution diversification storing new qualified solutions separately guiding archive, and, finally, exploiting around positions saved archive guided (GM) operator. dimensionally aware switch between these two operators guarantees convergence toward promising zones. M-MFO was evaluated CEC 2018 benchmark suite dimension 30 compared against seven well-known variants MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, ODSFMFO. Then, top four latest high-performing were considered experiments with different dimensions, 30, 50, 100. experimental evaluations proved provides exploration ability maintenance employing strategy archive. addition, statistical analyzed Friedman test performance contender algorithms used experiments.