Passos, Leandro AparecidoRodrigues, DouglasPapa, Joao Paulo [UNESP]2019-10-062019-10-062019-06-012019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, p. 3014-3021.http://hdl.handle.net/11449/190606Fitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based Backtracking Search Optimization Algorithm, a variant of the standard Backtracking Search Optimization Algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well.3014-3021engBacktracking Search Optimization AlgorithmMeta-heuristicsQuaternionsTrabalho apresentado em evento10.1109/CEC.2019.8790209Acesso restrito2-s2.0-85071311630