Novel differential evolution algorithm with spatial evolution rules

In order to reduce the pressure of parameter selection and avoid trapping into the local opti-mum, a novel differential evolution ( DE) algorithm without crossover rate is proposed.Through em-bedding cellular automata into the DE algorithm, those interactions among vectors are restricted within cellular structure of neighbors while the cell own evolution, which may be used to balance the tradeoff between exploration and exploitation and then tune the selection pressure.And further more, the orthogonal crossover without crossover rate is used instead of the binomial crossover, which can maintain the population diversity and accelerate the convergence rate.Experimental stud-ies are carried out on a suite of 7 bound-constrained numerical benchmark functions.The results show that the proposed algorithm has better capability of maintaining the population diversity and fas-ter convergence than the classical differential evolution and several classic differential evolution vari-ants.