<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Ranged Subgroup Particle Swarm Optimization for Localizing Multiple Odor Sources

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"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...

Ranged Subgroup Particle Swarm Optimization for Localizing Multiple Odor Sources

Online veröffentlicht: 13. Dez. 2017

Seitenbereich: 411 - 442

DOI: https://doi.org/10.21307/ijssis-2017-401

Schlüsselwörter
Modified Particle Swarm Optimization, Multiple Odor Sources Localization, Parallel Search, Subgroup, Open Dynamic Engine, Real Life Scenario

© 2010 W. Jatmiko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Ranged Subgroup Particle Swarm Optimization for Localizing Multiple Odor Sources

Online veröffentlicht: 13. Dez. 2017

Seitenbereich: 411 - 442

DOI: https://doi.org/10.21307/ijssis-2017-401

SchlüsselwörterModified Particle Swarm Optimization, Multiple Odor Sources Localization, Parallel Search, Subgroup, Open Dynamic Engine, Real Life Scenario

© 2010 W. Jatmiko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Modified Particle Swarm Optimization, Multiple Odor Sources Localization, Parallel Search, Subgroup, Open Dynamic Engine, Real Life Scenario