A small world algorithm for high-dimensional function optimization

In this paper, we describe a new small world optimization algorithm for obtaining satisfactory solution for high-dimensional function. Based on the small world phenomenon which is revealed in Milgram´s sociological experiment, some operators with decimal-coding strategy are proposed, and then an ¿imitated society¿ decimal-coding small world optimization algorithm (DSWOA) is designed to solve high-dimensional function optimization. Compared with the corresponding evolution algorithms, such as orthogonal genetic algorithm with quantization (OGA/Q), the simulation results of several benchmark functions with high dimension show that DSWOA can acquire satisfied solution, has also a better stability, and a fast convergence rate. Therefore, it is feasible to solve high-dimensional optimization problems.