“…Additionally, the metaheuristic algorithms are capable of escaping from local minima. Genetic algorithm (GA) [7-12, 20, 23, 25, 39], tabu search algorithm (TSA) [23,30], modified touring ant colony algorithm (MTACO) [13], particle swarm optimization (PSO) [15,30,33], bees algorithm (BA) [16,29], bacterial foraging algorithm (BFA) [19,24], clonal selection algorithm (CLONALG) [21], plant growth simulation algorithm (PGSA) [26], differential evolution (DE) algorithm [14,18,27], biogeography based optimization (BBO) [28], multiobjective DE (MODE) [30], memetic algorithm (MA) [17,23,30], nondominated sorting GA-2 (NSGA-2) [30], decomposition with DE (MOEA/D-DE) [30], comprehensive learning PSO (CLPSO) [31], seeker optimization algorithm (SOA) [32], invasive weed optimization (IWO) algorithm [34], harmony search algorithm (HSA) [35], firefly algorithm (FA) [36,38], cuckoo search (CS) algorithm [37,42], differential search algorithm (DSA) [40], cat swarm optimization (CSO) [41], and mean variance mapping optimization (MVMO) [43] can be given as the examples of these metaheuristic algorithms used for pattern nulling.…”