CSA/IE: Novel Clonal Selection Algorithm with Information Exchange for High Dimensional Global Optimization Problems

2012 ◽  
pp. 218-231 ◽  
Author(s):  
Zixing Cai ◽  
Xingbao Liu ◽  
Xiaoping Ren
Keyword(s):  
Global Optimization ◽  
Information Exchange ◽  
Optimization Problems ◽  
Clonal Selection ◽  
High Dimensional ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION

2010 ◽  
Vol 19 (01) ◽  
pp. 19-37 ◽  
Author(s):  
MAOGUO GONG ◽  
LICHENG JIAO ◽  
JIE YANG ◽  
FANG LIU
Keyword(s):  
Local Search ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Benchmark Problems ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Tournament Selection ◽  
Selection Operator ◽  
Lamarckian Learning

In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.

A Nonlinear Optimal Iterative Learning Control Algorithm Based on RBF Neural Network and Clonal Selection Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 1225-1229
Author(s):  
Heng Jie Li ◽  
Xiao Hong Hao ◽  
Xi Ping Pei
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Rbf Neural Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm

Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration; another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.

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