Abstract: A natural computing method based on spatial division search strategy is proposed. The strategy can map the high-dimensional space to the three-dimensional Cartesian coordinate system by grouping the dimensional space into a group of three dimensions. The individual after spatial segmentation is numbered into subindividual, to increases the particle number while reducing the dimension, thus the individual is distributed over wider search space to effectively increases the diversity of the population. The algorithm iterates to a certain extent and can synthesize the individual into the original individual through the numbered index. By calculating the fitness value, some poor individuals can be deleted to balance the time efficiency and speed up the running time. At the end of the iteration, the global optimal position of individual in the group can be found through the numbered index to synthesize the fitness value of the optimal individual output, which makes the algorithm have a better ability to search for the optimization. The convergence of the strategy is analyzed by Markov chain. The spatial division search strategy is applied to PSO, CA and DE, and its performance is verified in the standard test functions. Experimental results show that the proposed strategy can improve the convergence speed and optimization ability obviously.

Key words: space division, numbered particle, natural calculation, diversity preservation