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Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization

Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization

Upma Jain, Ritu Tiwari, W. Wilfred Godfrey
Copyright: © 2019 |Pages: 27
ISBN13: 9781522552765|ISBN10: 1522552766|EISBN13: 9781522552772
DOI: 10.4018/978-1-5225-5276-5.ch010
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MLA

Jain, Upma, et al. "Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization." Novel Design and Applications of Robotics Technologies, edited by Dan Zhang and Bin Wei, IGI Global, 2019, pp. 276-302. https://doi.org/10.4018/978-1-5225-5276-5.ch010

APA

Jain, U., Tiwari, R., & Godfrey, W. W. (2019). Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization. In D. Zhang & B. Wei (Eds.), Novel Design and Applications of Robotics Technologies (pp. 276-302). IGI Global. https://doi.org/10.4018/978-1-5225-5276-5.ch010

Chicago

Jain, Upma, Ritu Tiwari, and W. Wilfred Godfrey. "Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization." In Novel Design and Applications of Robotics Technologies, edited by Dan Zhang and Bin Wei, 276-302. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-5276-5.ch010

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Abstract

This chapter concerns the problem of odor source localization by a team of mobile robots. A brief overview of odor source localization is given which is followed by related work. Three methods are proposed for odor source localization. These methods are largely inspired by gravitational search algorithm, grey wolf optimizer, and particle swarm optimization. Objective of the proposed approaches is to reduce the time required to localize the odor source by a team of mobile robots. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. Robots start search from the corner of the workspace. As robots enter in the vicinity of plume area, they form groups using K-nearest neighbor algorithm. To avoid stagnation of the robots at local optima, search counter concept is used. Proposed approaches are tested and validated through simulation.

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