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Mathematical Problems in Engineering
Volume 2014, Article ID 434972, 7 pages
Research Article

An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

School of Computer and Communication, LanZhou University of Technology, Lanzhou 730050, China

Received 12 November 2013; Revised 22 January 2014; Accepted 21 February 2014; Published 2 April 2014

Academic Editor: Ge Guo

Copyright © 2014 Ming Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.