HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

• Main Authors: Kappagantula, S., Vojjala, S., Iyer, A.A., Velidi, G., Emani, S., Vandrangi, S.K.
• Format: Article
• Published: Regional Association for Security and crisis management 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37571/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172375520&doi=10.31181%2foresta%2f060204&partnerID=40&md5=9134d4f30814b2fa2ab6819b4c4ad65a

Inswarm robotics, a group ofrobots coordinatewitheachother tosolvea problem. Swarm systems can be heterogeneous or homogeneous. Heterogeneous swarms consist of multiple types of robots as opposed to Homogeneous swarms, whichare made up of identical robots. There are cases where a Heterogeneous swarm system may consist of multiple Homogeneous swarm systems. Swarm robots can be used for a variety of applications. Swarm robots are majorly used in applications involving the exploration of unknown environments. Swarm systems are dynamic and intelligent. Swarm Intelligence is inspired by naturally occurring swarm systems suchas Ant Colony, Bees Hive, or Bats. The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. In this paper, we study the advantages of fusing the Meta-Heuristic Bat Algorithm with Heuristic Optimization. We have implemented the Meta- Heuristic Bat Algorithm and tested it on a heterogeneous swarm. The same swarm has also been tested by segregating it into different homogeneous swarms by subjecting the heterogeneous swarm to a heuristic optimization. © 2023 Regional Association for Security and crisis management.