Energy-based acoustic localization by improved elephant herding optimization
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Data
2020
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IEEE Access
Resumo
The present work proposes a new approach to address the energy based acoustic localization
problem. The proposed approach represents an enhanced version of evolutionary optimization based on
Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of
random initialization of elephant population, we exploit particularities of the problem at hand to develop
an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point
are used to determine the regions in which a source is most likely to be located at. Secondly, rather than
letting elephants to simply wander around in their search for an update in the source location, we base
their motion on a local search scheme which is found on a discrete gradient method. Such a methodology
significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational
cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show
that the enhanced algorithm significantly outperforms the standard EHO method for low noise and matches
its performance for high noise, in terms of localization accuracy. Moreover, they show that the proposed
enhanced version requires significantly less number of iterations to converge.
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IEEE Access
Palavras-chave
ENGENHARIA ELETROTÉCNICA, ELECTROTECHNICAL ENGINEERING, ENGENHARIA ACÚSTICA, ACOUSTICAL ENGINEERING, OTIMIZAÇÃO, OPTIMIZATION