A small world algorithm for high-dimensional function optimization

Authors:
Xiaohu Li;Jinhua Zhang;Sunan Wang;Maolin Li;Kunpeng Li
Affiliations:
Mechanical Engineering Department and Engineering Workshop, Xi'an Jiaotong University, Xi'an, Shanxi, China;Mechanical Engineering Department, Xi'an Jiaotong University, Xi'an, Shanxi, China;Mechanical Engineering Department, Xi'an Jiaotong University, Xi'an, Shanxi, China;Mechanical Engineering Department, Xi'an Jiaotong University, Xi'an, Shanxi, China;Mechanical Engineering Department, Xi'an Jiaotong University, Xi'an, Shanxi, China
Venue:
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Year:
2009

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Abstract

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.