“…Until now, several researchers have developed various metaheuristics, which find their source of inspiration in nature. Some of them are as follows: genetic algorithm (GA) (first described by John Henry Holland in the 1960s) is based on the natural selection [2][3][4], ant colony optimization (ACO) (initially proposed by Marco Dorigo in 1992) finds its inspiration from ant colony behavior [5][6][7], particle swarm optimization (PSO) (developed by James Kennedy and Russell C. Eberhart in 1995) is based on social flocking behavior of birds [8][9][10], artificial bee colony (ABC) (invented by Dervis Karaboga in 2005) is inspired by intelligent foraging behavior of honey bee swarm [11,12], magnetic charged system search [13], charged system search [14], firefly algorithm (FA) (created by Xin-She Yang in 2008) is inspired by the flashing light pattern of fireflies [15][16][17][18], biogeography-based optimization (BBO) (introduced by Dan Simon in 2008) is based on the equilibrium theory of island biogeography [19][20][21], bat algorithm (BA) (proposed by Xin-She Yang in 2010) is a metaheuristic algorithm, which is inspired by the echolocation behavior of microbats [22,23], and more recently, butterfly optimization algorithm (BOA) (developed by Arora and Singh in 2015) which finds its source of inspiration in food foraging behavior of butterflies [24].…”