HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION
| Autor: | Sivayazi Kappagantula, Saipranav Vojjala, Aditya Arun Iyer, Gurunadh Velidi, Sampath Emani, Seshu Kumar Vandrangi |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Operational Research in Engineering Sciences: Theory and Applications, Vol 6, Iss 2 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2620-1607 2620-1747 |
| Popis: | In swarm robotics, a group of robots coordinate with each other to solve a problem. Swarm systems can be heterogeneous or homogeneous. Heterogeneous swarms consist of multiple types of robots as opposed to Homogeneous swarms, which are 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 such as 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. |
| Databáze: | Directory of Open Access Journals |
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