Abstract: This paper designed a fast search algorithm called Small World Optimization(SWO) which was inspired by the hierarchical categorization tree model and multi categories method based on small world theory.The solution space can be divided into the hierarchical categorization tree model using mask rule in binary coding. Two bijective mapping solution space were adopted to establish multi categories method. SWO can find the optimal solution in the designed small world network by short and long distance neighbor relationship as pushing the mail to target. SWO was tested via a benchmark test functions in a simulation and the corresponding results show that two bijective mapping space can avoid the algorithm falling into early maturity and the long distance neighbor relationship can accelerate the convergence rate. Compared with the most popular optimization algorithm as genetic algorithm(GA)，Particle Swarm Optimization (PSO) and Difference Algorithm(DE)，the SWO algorithm is endowed with faster convergence ability to solve complex optimization problems.

Key words: Small world optimization, Hierarchical categorization tree model, Multi categories standard model, Distributed searching