Ranged Subgroup Particle Swarm Optimization for Localizing Multiple Odor Sources

A new algorithm based on Modified Particle Swarm Optimization (MPSO) that follows is a local gradient of a chemical concentration within a plume and follows the direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi...

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Bibliographic Details
Published in:International journal on smart sensing and intelligent systems 2010-01, Vol.3 (3), p.411-442
Main Authors: Jatmiko, W., Pambuko, W., Febrian, A., Mursanto, P., Muis, A., Kusumoputro, B., Sekiyama, K., Fukuda, T.
Format: Article
Language:English
Subjects:
Algorithms
Concentration gradient
Modified Particle Swarm Optimization
Multiple Odor Sources Localization
Odors
Open Dynamic Engine
Parallel Search
Particle swarm optimization
Real Life Scenario
Robots
Subgroup
Subgroups
Wind speed
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Summary:A new algorithm based on Modified Particle Swarm Optimization (MPSO) that follows is a local gradient of a chemical concentration within a plume and follows the direction of the wind velocity is investigated. Moreover, the niche or parallel search characteristic is adopted on MPSO to solve the multi-peak and multi-source problem. When using parallel MPSO, subgroup of robot is introduced then each subgroup can locate the odor source. Unfortunately, there is a possibility that more that one subgroup locates one odor sources. This is inefficient because other subgroups locate other source, then we proposed a ranged subgroup method for coping for that problem, then the searching performance will increase. Finally ODE (Open Dynamics Engine) library is used for physical modeling of the robot like friction, balancing moment and others so that the simulation adequate to accurately address the real life scenario.
ISSN:1178-5608
1178-5608
DOI:10.21307/ijssis-2017-401