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Solving real-world optimization problems is considered a challenging task. This is due to the variability of the characteristics in objective functions, the presence of enormous number of local optima within the search space and highly nonlinear constraints with large number of variables. The advances on this type of problems are of capital importance for many researchers to develop new efficient...
Many Differential Evolution algorithms are introduced in the literature to solve optimization problems with diverse set of characteristics. In this paper, we propose an extension of the previously published paper LSHADE-EpSin that was ranked as the joint winner in the real-parameter single objective optimization competition, CEC 2016. The contribution of this work constitutes two major modifications...
An effective and efficient self-adaptation framework is proposed to improve the performance of the L-SHADE algorithm by providing successful alternative adaptation for the selection of control parameters. The proposed algorithm, namely LSHADE-EpSin, uses a new ensemble sinusoidal approach to automatically adapt the values of the scaling factor of the Differential Evolution algorithm. This ensemble...
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