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A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization

A Hybridization of Gravitational Search Algorithm and Particle Swarm Optimization for Odor Source Localization
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Author(s): Upma Jain (ABV-IIITM Gwalior, Gwalior, India), W. Wilfred Godfrey (ABV-IIITM Gwalior, Gwalior, India)and Ritu Tiwari (ABV-IIITM Gwalior, Gwalior, India)
Copyright: 2017
Volume: 5
Issue: 1
Pages: 14
Source title: International Journal of Robotics Applications and Technologies (IJRAT)
Editor(s)-in-Chief: Dan Zhang (York University, Canada)
DOI: 10.4018/IJRAT.2017010102

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Abstract

This paper concerns with the problem of odor source localization by a team of mobile robots. The authors propose two methods for odor source localization which are largely inspired from gravitational search algorithm and particle swarm optimization. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. As robots enter in the vicinity of plume area they form groups using K-nearest neighbor algorithm. The problem of local optima is handled through the use of search counter concept. The proposed approaches are tested and validated through simulation.

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