Swarm Intelligence Algorithms - A Survey - Meghana, L; Jaya, R (International Journal of Computer Sciences and Engineering, 2025)
Swarm intelligence is an exploration ground that simulates the mutual behavior in groups of insects or animals. Some algorithms ascending from such models have been proposed to solve a widespread range of difficult optimization problems. Typical swarm intelligence algorithms including Particle Swarm Optimization (PSO), Ant Colony System (ACS), Honey bee mating optimization (HBMO), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), Bat algorithm (BA), and Firefly algorithm, have been proven to be noble methods to address difficult optimization problems under static environments. Maximum SI algorithms have been established to discourse static optimization problems and hence, they can meet on the optimum solution powerfully. Swarm intelligence (SI) is built based on the combined characteristics of self-systematized systems. Furthermore the uses to conventional optimization problems, SI can also be used in monitoring robots and automated vehicles, forecasting social behaviors, improving the telecommunication and computer networks, etc. To be precise, the usage of swarm optimization can be applied to the various fields in engineering.