### Abstract

This paper addresses the problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charge PSO, which is another extension of the PSO has also been applied to solve dynamic problem. Odor source localization is an interesting application in dynamic problem. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such a kind of dynamic environment problem.

Original language | English |
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Title of host publication | Proceedings of IEEE Sensors |

Pages | 254-257 |

Number of pages | 4 |

Volume | 2005 |

DOIs | |

Publication status | Published - 2005 |

Externally published | Yes |

Event | Fourth IEEE Conference on Sensors 2005 - Irvine, CA, United States Duration: Oct 31 2005 → Nov 3 2005 |

### Other

Other | Fourth IEEE Conference on Sensors 2005 |
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Country | United States |

City | Irvine, CA |

Period | 10/31/05 → 11/3/05 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering (miscellaneous)
- Electrical and Electronic Engineering

### Cite this

*Proceedings of IEEE Sensors*(Vol. 2005, pp. 254-257). [1597684] https://doi.org/10.1109/ICSENS.2005.1597684

**Distributed odor source localization in dynamic environment.** / Jatmiko, W.; Ikemoto, Y.; Matsuno, Takayuki; Fukuda, T.; Sekiyama, K.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of IEEE Sensors.*vol. 2005, 1597684, pp. 254-257, Fourth IEEE Conference on Sensors 2005, Irvine, CA, United States, 10/31/05. https://doi.org/10.1109/ICSENS.2005.1597684

}

TY - GEN

T1 - Distributed odor source localization in dynamic environment

AU - Jatmiko, W.

AU - Ikemoto, Y.

AU - Matsuno, Takayuki

AU - Fukuda, T.

AU - Sekiyama, K.

PY - 2005

Y1 - 2005

N2 - This paper addresses the problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charge PSO, which is another extension of the PSO has also been applied to solve dynamic problem. Odor source localization is an interesting application in dynamic problem. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such a kind of dynamic environment problem.

AB - This paper addresses the problem of odor source localization in a dynamic environment, which means the odor distribution is changing over time. Modification Particle Swarm Optimization is a well-known algorithm, which can continuously track a changing optimum over time. PSO can be improved or adapted by incorporating the change detection and responding mechanisms for solving dynamic problems. Charge PSO, which is another extension of the PSO has also been applied to solve dynamic problem. Odor source localization is an interesting application in dynamic problem. We will adopt two types of PSO modification concepts to develop a new algorithm in order to control autonomous vehicles. Then we develop odor localization algorithm, and simulations to show that the new approach can solve such a kind of dynamic environment problem.

UR - http://www.scopus.com/inward/record.url?scp=33847293850&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33847293850&partnerID=8YFLogxK

U2 - 10.1109/ICSENS.2005.1597684

DO - 10.1109/ICSENS.2005.1597684

M3 - Conference contribution

AN - SCOPUS:33847293850

SN - 0780390563

SN - 9780780390560

VL - 2005

SP - 254

EP - 257

BT - Proceedings of IEEE Sensors

ER -