ABSTRACT
Nature is an intrinsic source of inspiration for researchers and scientists working in the area of optimization. This chapter introduces three swarm-intelligence-based nature-inspired algorithms namely the artificial bee colony (ABC) algorithm, firefly algorithm (FA), and bat algorithm (BA). The artificial bee colony algorithm, which imitates the intellectual food-foraging behavior of honey bees, came into existence in 2005. The firefly algorithm is motivated by the flashing behavior of fireflies, and the bat algorithm shows the echolocation behavior of micro-bats. These population-based algorithms are used to get rid of complex real-world optimization problems that are not soluble by conventional methods. In-depth evaluation and analysis of these swarm optimization algorithms are given in this chapter. The chapter also provides a deeper and broader outlook to help researchers and students to use these algorithms to solve real-world complex optimization problems with their recent and popular variants.
