An Improved Dynamical Evolutionary Algorithm and Its Application

2008 ◽  
Author(s):  
Yi Jiang ◽  
Ling Wang ◽  
Li Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Dynamical Evolutionary Algorithm

Hybrid Dynamical Evolutionary Algorithm and Time Complexity Analysis

2012 ◽  
Vol 263-266 ◽  
pp. 2344-2348
Author(s):  
Hui Ying Li ◽  
Yi Lai Zhang ◽  
Xing Xu
Keyword(s):  
Evolutionary Algorithm ◽  
Time Complexity ◽  
Complexity Analysis ◽  
Optimization Problems ◽  
Function Optimization ◽  
Numerical Function ◽  
Similar Time ◽  
Local Optima ◽  
Minimization Problems ◽  
Dynamical Evolutionary Algorithm

The dynamical evolutionary algorithm (DEA) is a new evolutionary algorithm based on the theory of statistical mechanics, however, DEA converges slowly and often converge at local optima for some function optimization problems. In this paper, a hybrid dynamical evolutionary algorithm (HDEA) with multi-parent crossover and differential evolution mutation is proposed for accelerating convergence velocity and easily escaping suboptimal solutions. Moreover, the population of HDEA is initialized by chaos. In order to confirm the effectiveness of our algorithm, HDEA is applied to solve the typical numerical function minimization problems. The computational complexity of HDEA is analyzed, and the experimental results show that HDEA outperforms the DEA in the aspect of convergence velocity and precision, even the two algorithms have the similar time complexity.

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