Title :
The application of improved evolutionary strategy algorithm in optimization
Author :
Yang, Li ; Jia, Qing-lan
Author_Institution :
Dept. of Math., Cangzhou Normal Univ., Cangzhou, China
Abstract :
In this paper, improved evolutionary strategy algorithm is combined with evolutionary strategy algorithm and the simulated annealing algorithm to deal with the optimization of differentiable function and non-differentiable function. In the improved evolutionary strategy algorithm, the simulated annealing algorithm is dissolved into evolutionary strategy as an operator, giving full play to the advantages of the simulated annealing algorithm and evolutionary strategy to effectively solve the sensitivity and inefficiency in the traditional algorithm. The numerical simulation results show that it has good convergence and reliability, a higher convergence speed and accuracy as well as a good adaptability for solving optimization problems.
Keywords :
convergence; evolutionary computation; simulated annealing; convergence speed; evolutionary strategy algorithm; nondifferentiable function; numerical simulation; operator; optimization problem; reliability; simulated annealing algorithm; Abstracts; Simulated annealing; Differentiable function; Evolutionary strategy; Non-differentiable function; Simulated Annealing Algorithm;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
Print_ISBN :
978-1-4673-1484-8
DOI :
10.1109/ICMLC.2012.6359528