Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems

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 that have been added to enhance the performance: ensemble of sinusoidal approaches based on performance adaptation and covariance matrix learning for the crossover operator. Two sinusoidal waves have been used to adapt the scaling factor: non-adaptive sinusoidal decreasing adjustment and an adaptive sinusoidal increasing adjustment. Instead of choosing one of the sinusoidal waves randomly, a performance adaptation scheme based on earlier success is used in this work. Moreover, covariance matrix learning with Euclidean neighborhood is used for the crossover operator to establish a suitable coordinate system, and to enhance the capability of LSHADE-EpSin to tackle problems with high correlation between the variables. The proposed algorithm, namely LSHADE-cnEpSin, is tested on the IEEE CEC2017 problems used in the Special Session and Competitions on Single Objective Bound Constrained Real-Parameter Single Objective Optimization. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to other state-of-the-art algorithms.