Buradasınız

Anasayfa » Content

YAPAY BAĞIŞIKLIK SİSTEMİNİN ÇOKLU ETMEN BENZETİM ORTAMINDA REALİZE EDİLMESİ VE GEZGİN SATICI PROBLEMİNE UYGULANMASI

REALIZING ARTIFICIAL IMMUNE SYSTEM IN A MULTI AGENT SIMULATION ENVIRONMENT AND AN APPLICATION TO TRAVELLING SALESMEN PROBLEM

Journal Name:

  • Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

Publication Year:

  • 2012

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Although many meta-heuristic algorithms were developed for solving combinatorial optimization problems, very few of them were realized in an agent based environment. Especially the algorithms which model dynamics of Artificial Immune Systems (AIS) are population based approaches with adaptability characteristics, therefore AIS can be better realized in an agent based modeling environment. For this purpose first time in the literature a clonal selection algorithm which is an AIS based algorithm is modeled in a multi-agent environment for solving the travelling salesmen problem which is a combinatorial optimization problem. In order to observe the behavior of the algorithm, simulation experiments are carried out on several test problems. Netlogo software is utilized for developing agent based models and simulation tests. Moreover, receptor change process and crossover mechanisms are integrated into the proposed model in order to improve the performance of the classical clonal selection algorithm. It is shown that there is a high potential to obtain good solution by making use of agent oriented approaches which more realistically model the natural phenomenon.
Bookmark/Search this post with
Abstract (Original Language): 
Kombinatoryal optimizasyon problemlerinin çözümü için geliştirilen birçok meta-sezgisel algoritma bulunmasına rağmen bu algoritmaları etmen tabanlı modelleme ortamında realize eden çalışmaların sayısı çok azdır. Özellikle Yapay Bağışıklık Sisteminin (YBS) dinamiklerini modelleyen algoritmaların adaptasyon yeteneğine sahip popülasyon tabanlı yaklaşımlar olması, YBS’nin etmen tabanlı modelleme ortamında gerçeğe daha yakın bir şekilde realize edilmesini sağlayacaktır. Bu amaçla ilk defa mevcut çalışmada, bir kombinatoryal optimizasyon problemi olan gezgin satıcı probleminin (GSP) modellenmesi ve çözümü için yapay bağışıklık sistemi algoritmalarından klonal seçim algoritması çoklu etmen benzetim ortamında modellenmiş ve algoritmanın gösterdiği davranışın incelenmesi için farklı GSP setleri üzerinde benzetim deneyleri gerçekleştirilmiştir. Etmen tabanlı modellerin geliştirilmesi ve benzetim testlerinin yapılabilmesi için Netlogo yazılımı kullanılmıştır. Ayrıca geleneksel klonal seçim algoritmasının performansını arttırmak için reseptör değişim süreci ve çaprazlama mekanizması önerilen modele entegre edilmiş ve doğal oluşları daha gerçekçi modelleyebilen etmen tabanlı yaklaşımlar ile de etkin çözümler elde edilebileceği gösterilmiştir.
FULL TEXT (PDF): 
PDF icon arastirmax-yapay-bagisiklik-sisteminin-coklu-etmen-benzetim-ortaminda-realize-edilmesi-gezgin-satici-problemine-uygulanmasi.pdf
  • 4
901
909
  • Turkish

REFERENCES

References: 

1. De Castro, L. ve Von Zuben, F., “Learning and
Optimization Using The Clonal Selection
Principle”, Evolutionary Computation, Cilt 6,
Sayı 3, 239-251, 2002.
2. Gao, S., Dai, H., Yang, G. ve Tang, Z., “A Novel
Clonal Selection Algorithm and Its Application to
Traveling Salesman Problem”, IEICE Trans.
Fundamentals, Cilt E90–A, No 10, 2007.
3. Dai, H., Yang, Y. ve Li, C., “Improved Quantum
Crossover Based Clonal Selection Algorithm”,
Third International Conference on Intelligent
Networks and Intelligent Systems, Shenyang -
Çin, 366-369, 01-03 Kasım 2010.
4. Dai, H., Yang, Y. ve Li, C., “Distance
Maintaining Compact Quantum Crossover Based
Clonal Selection Algorithm”, Journal of
Convergence Information Technology, Cilt 5,
No 10, 56-65, 2010.
5. Machado, R.B., Boukerche, A., Sobral, J.B.M.,
Juca, K.R.L. ve Notare, M.S.M.A., “A Hybrid
Artificial Immune and Mobile Agent Intrusion
Detection Based Model for Computer Network
Operations”, 19th IEEE International Parallel
and Distributed Processing Symposium,
Denver – ABD, 04-08 Nisan 2005.
6. Grilo, A., Caetano, A. ve Rosa, A., “Agent Based
Artificial Immune System”, Genetic and
Evolutionary Computation Congress (Proc.
GECCO-01), San Francisco – ABD, Cilt LBP,
145-151, 07-11 Temmuz 2001.
7. Ou, C.M. Wang, Y.T. ve Ou, C.R., “Intrusion
Detection Systems Adapted From Agent-Based
Artificial Immune Systems”, IEEE
International Conference on Fuzzy Systems,
Taipai – Tayvan, 115-122, 27-30 Haziran 2011.
8. Mendao, M., Timmis, J., Andrews, P.S. ve
Davies, M., “The Immune System in Pieces:
Computational Lessons from Degeneracy in the
Immune System”, IEEE Symposium on
Foundations of Computational Intelligence,
Havai, 394-400, 01-05 Nisan 2007.
9. Sathyanath, S. ve Sahin, F., “Application of
Artificial Immune System Based Intelligent Multi
Agent Model to a Mine Detection Problem”,
IEEE International Conference on Systems,
Man and Cybernetics, Hammamet – Tunus, 06-
09 Ekim 2002.
10. Mamady, D., Tan, G., Toure, M.L. ve Alfawaer,
Z.M., “An Artificial Immune System Based
Multi-Agent Robotic Cooperation”, Novel
Algorithms and Techniques in
Telecommunications, Automation and
Industrial Electronics, 60-67, 2008.
11. Chingtham, T.S., Sahoo, G. ve Ghose, M.K., “An
Artificial Immune System Model for Multi
Agents Resource Sharing in Distributed
Environments”, International Journal on
Computer Sciences and Engineering, Cilt 2, No
5, 1813-1818, 2010.
12. Singh, C.T. ve Nair, S.B., “An Artificial Immune
System for a Multi-Agent Robotics System”,
Word Academy of Science, Eng. and
Technology, Cilt 11, Sayı 3, 6-9, 2005.
13. Dasgupta, D. ve Nino, F., Immunological
Computation: Theory and Applications,
Auerbach Publications, 2008.
14. Timmis. J., Hone, A., Stibor, T. ve Clark, E.,
“Theoretical Advances in Artificial Immune
Systems”, Theoretical Computer Science, Cilt
403, Sayı 1, 11-32, 2008.
15. Dasgupta, D., “Information Processing
Mechanisms of the Immune System”, New Ideas
in Optimization, Corne, D., Dorigo, M. ve
Glover, F., McGraw-Hill, ABD, 1999.
16. De Castro, L. N. ve Timmis, J., Artificial
Immune Systems: A New Computational
Intelligence Approach, Springer, ABD, 2002.
17. Macal, C.M. ve North, M.J.; “Agent-based
Modeling and Simulation”, Winter Simulation
Conference 2009, Austin – ABD, 86-98, 13-16
Aralık 2009.
18. Siebers, P.O. ve Aickelin, U., “Introduction to
Multi Agent Simulation”, Encylopedia of
Decision Making and Decision Support
Technologies, Editör: Adam, F., Information
Science Reference, ABD, 554-564, 2007.
19. Siebers, P.O., “Lecture Notes”,
http://www.cs.nott.ac.uk/~pos/index.html.
20. Bradshaw, J.M., Software Agents, The MIT
Press, ABD, 1997.
21. Jennings, N., “On Agent-based Software
Engineering”, Artificial Intelligence, Cilt 117,
Sayı 2, 277-296, 2000.
22. Pelta, D., Cruz C. ve Gonzalez, J.R., “A Study on
Diversity and Cooperation in a Multiagent
Strategy for Dynamic Optimization Problems”,
International Journal of Intelligent Systems,
Cilt 24, Sayı 7, 844–861, 2009.
23. Terna P., “The epidemic of innovation – playing
around with an agent-based model”, Economics
of Innovation and New Technology, Cilt 18, No
7, 707–728, 2009.
24. Sakellariou, I., Kefalas, P. ve Stamatopoulou, I.,
“Enhancing NetLogo to Simulate BDI
Communicating Agents”, SETN '08 Proceedings
of the 5th Hellenic Conference on Artificial
Intelligence: Theories, Models and App.,
Syros, 263-275, 02-04,10, 2008.
25. Tisue, S. ve Wilensky, U., “NetLogo: design and
implementation of a multi-agent modeling
environment”, Proceedings of Agent, Chicago –
ABD, Ekim 2004.
26. Somhom, S., Modares, A. ve Enkawa, T., “A
Self-organizing Model for the Traveling
Salesman Problem”, J. of the OR Society, Cilt
48, No 9, 919–928, 1997.
27. Cochrane, E. M., ve Beasley, J. E., “The Coadaptive
Neural Network Approach to the
Euclidean Traveling Salesman Problem”, Neural
Networks, Cilt 16,10, 1499–1525, 2003

Thank you for copying data from http://www.arastirmax.com