Solving the Associated Weakness of Biogeography-Based Optimization Algorithm

Abstract

Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based

A modified butterfly optimization algorithm for mechanical design optimization problems

“…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].…”