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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 291298, 11 pages
http://dx.doi.org/10.1155/2015/291298
Research Article

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

1School of Mathematics and Computer Science, Zunyi Normal College, Zunyi 563002, China
2College of Management, Shenzhen University, Shenzhen 518060, China
3College of Life Science, Zunyi Normal College, Zunyi 563002, China

Received 2 September 2015; Accepted 29 October 2015

Academic Editor: Felix T. S. Chan

Copyright © 2015 Yanmin Liu 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.

Abstract

Swarm intelligence (SI) is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO) and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence.