A hybrid adaptive coevolutionary differential evolution algorithm for large-scale optimization

In this paper, we propose a new algorithm, named HACC-D, for large scale optimization problems. The motivation is to improve the optimization method for the subcomponents in the cooperative coevolution framework. In the new HACC-D algorithm, an algorithm selection method named hybrid adaptive optimization strategy is used. It is aimed to hybridize the superiority of two very efficient differential evolution algorithms, JADE and SaNSDE, as the subcomponent optimization algorithm of the cooperative coevolution. In the beginning stage, the novel strategy evolves the initial population with JADE and SaNSDE as the subcomponent optimization algorithm for a certain number of iterations separately. Then the one obtained better fitness value will be chosen to be the subcomponent optimization algorithm for the following evolution process. In the later stage of evolution, the selected algorithm may be trapped in a local optimum or lose its ability to make further progress. So it exchanges the subcomponent optimization algorithm with the other one when there is no improvement in the fitness every certain number of iterations. The proposed HACC-D algorithm is evaluated on CEC´2010 benchmark functions for large scale global optimization.