HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

Sivayazi Kappagantula; Saipranav Vojjala; Aditya Arun Iyer; Gurunadh Velidi; Sampath Emani; Seshu Kumar Vandrangi

doi:10.31181/oresta/060204

HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

Sivayazi Kappagantula^*
, Saipranav Vojjala
, Aditya Arun Iyer
, Gurunadh Velidi
, Sampath Emani
, Seshu Kumar Vandrangi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	52-77
Number of pages	26
Journal	Operational Research in Engineering Sciences: Theory and Applications
Volume	6
Issue number	2
DOIs	https://doi.org/10.31181/oresta/060204
Publication status	Published - 2023

All Science Journal Classification (ASJC) codes

Engineering (miscellaneous)
Management Science and Operations Research

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10.31181/oresta/060204

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title = "HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION",

abstract = "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.",

author = "Sivayazi Kappagantula and Saipranav Vojjala and Iyer, \{Aditya Arun\} and Gurunadh Velidi and Sampath Emani and Vandrangi, \{Seshu Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2023 Regional Association for Security and crisis management.",

year = "2023",

doi = "10.31181/oresta/060204",

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AU - Vojjala, Saipranav

AU - Iyer, Aditya Arun

AU - Velidi, Gurunadh

AU - Emani, Sampath

AU - Vandrangi, Seshu Kumar

PY - 2023

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N2 - 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.

AB - 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.

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HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

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