MOVLAB - Artigos de Revistas Internacionais com Arbitragem Científica
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Item Elephant Herding Optimization for Energy-Based Localization(MDPI, 2018) Correia, Sérgio; Beko, Marko; Cruz, Luís Alberto da Silva; Tomic, SlavisaThis work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.Item Energy-based acoustic localization by improved elephant herding optimization(IEEE Access, 2020) Correia, Sérgio; Beko, Marko; Tomic, Slavisa; Cruz, Luís Alberto da SilvaThe 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.