Commenced in January 2007
Paper Count: 30579
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
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063230Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1526
 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
 L.N. DeCastroand and F.J. VonZuben, "An Artificial Immune Network for Data Analysis".2001, In Data Mining. A Heuristic Approach
 S. Bachmayer,"Artificial Immune Systems: soft computing", 2006, vol. 7, pp. 69-86
 J. Timmis, "An Introduction to Artificial Immune Systems" 2004, ICARIS, vol. 7
 J. Timmis and C. Edmonds, "A Comment on opt-AiNET: An Immune Network Algorithm for Optimisation". soft computing, 2003 vol. 7.
 A.E. Eiben, and J. E. Smith, Introduction to Evolutionary computing. 2003,Springer.
 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.
 R. Javadzadeh.,Z. Afsahi, and M. R. Meybodi , "Hybrid Models based on Artificial Immune systems and Cellular Learning Automata. 2010, IASTED Technology Conference ,usa.