Improved Artificial Immune System Algorithm with Local Search
The Artificial immune systems algorithms are Meta
heuristic optimization method, which are used for clustering and
pattern recognition applications are abundantly. These algorithms in
multimodal optimization problems are more efficient than genetic
algorithms. A major drawback in these algorithms is their slow
convergence to global optimum and their weak stability can be
considered in various running of these algorithms. In this paper,
improved Artificial Immune System Algorithm is introduced for the
first time to overcome its problems of artificial immune system. That
use of the small size of a local search around the memory antibodies
is used for improving the algorithm efficiently. The credibility of the
proposed approach is evaluated by simulations, and it is shown that
the proposed approach achieves better results can be achieved
compared to the standard artificial immune system algorithms
[1] L.N. DeCastro and F.J. VonZuben, "Learning and Optimization Using
the Clonal Selection Principle".2002 IEEE Transactions on Evolutionary
Computation, vol. 6, pp. 239-251
[2] L.N. DeCastroand and F.J. VonZuben, "An Artificial Immune Network
for Data Analysis".2001, In Data Mining. A Heuristic Approach
[3] S. Bachmayer,"Artificial Immune Systems: soft computing", 2006, vol.
7, pp. 69-86
[4] J. Timmis, "An Introduction to Artificial Immune Systems" 2004,
ICARIS, vol. 7
[5] J. Timmis and C. Edmonds, "A Comment on opt-AiNET: An Immune
Network Algorithm for Optimisation". soft computing, 2003 vol. 7.
[6] A.E. Eiben, and J. E. Smith, Introduction to Evolutionary computing.
2003,Springer.
[7] R. Javadzadeh and M. R. Meybodi," Hybrid Models based on Artificial
Immune system and, Cellular Automata and Their Applications to
Optimization Problems", 2008, Technical Report, Computer
Engineering Department, Amirkabir University, Tehran, Iran.
[8] R. Javadzadeh.,Z. Afsahi, and M. R. Meybodi , "Hybrid Models based
on Artificial Immune systems and Cellular Learning Automata. 2010,
IASTED Technology Conference ,usa.
[1] L.N. DeCastro and F.J. VonZuben, "Learning and Optimization Using
the Clonal Selection Principle".2002 IEEE Transactions on Evolutionary
Computation, vol. 6, pp. 239-251
[2] L.N. DeCastroand and F.J. VonZuben, "An Artificial Immune Network
for Data Analysis".2001, In Data Mining. A Heuristic Approach
[3] S. Bachmayer,"Artificial Immune Systems: soft computing", 2006, vol.
7, pp. 69-86
[4] J. Timmis, "An Introduction to Artificial Immune Systems" 2004,
ICARIS, vol. 7
[5] J. Timmis and C. Edmonds, "A Comment on opt-AiNET: An Immune
Network Algorithm for Optimisation". soft computing, 2003 vol. 7.
[6] A.E. Eiben, and J. E. Smith, Introduction to Evolutionary computing.
2003,Springer.
[7] R. Javadzadeh and M. R. Meybodi," Hybrid Models based on Artificial
Immune system and, Cellular Automata and Their Applications to
Optimization Problems", 2008, Technical Report, Computer
Engineering Department, Amirkabir University, Tehran, Iran.
[8] R. Javadzadeh.,Z. Afsahi, and M. R. Meybodi , "Hybrid Models based
on Artificial Immune systems and Cellular Learning Automata. 2010,
IASTED Technology Conference ,usa.
@article{"International Journal of Information, Control and Computer Sciences:55688", author = "Ramin Javadzadeh. and Zahra Afsahi and MohammadReza Meybodi", title = "Improved Artificial Immune System Algorithm with Local Search", abstract = "The Artificial immune systems algorithms are Meta
heuristic optimization method, which are used for clustering and
pattern recognition applications are abundantly. These algorithms in
multimodal optimization problems are more efficient than genetic
algorithms. A major drawback in these algorithms is their slow
convergence to global optimum and their weak stability can be
considered in various running of these algorithms. In this paper,
improved Artificial Immune System Algorithm is introduced for the
first time to overcome its problems of artificial immune system. That
use of the small size of a local search around the memory antibodies
is used for improving the algorithm efficiently. The credibility of the
proposed approach is evaluated by simulations, and it is shown that
the proposed approach achieves better results can be achieved
compared to the standard artificial immune system algorithms", keywords = "Artificial immune system, Cellular Automata,Cellular learning automata, Cellular learning automata,, Local search,Optimization.", volume = "5", number = "11", pages = "1293-4", }