Doğadan Esinlenen Optimizasyon Algoritmaları ve Optimizasyon Algoritmalarının Optimizasyonu

Abstract

Matematiksel olarak Optimizasyon,  bir yada daha fazla bağımsız değişkene sahip olan bir fonksiyonun verilen kısıtlayıcı şartlar altındaki en iyi çözümünü arama işlemi olarak tanımlanabilir.  Optimizasyon problemlerini çözmek için sezgisel optimizasyon algoritmaları ve klasik çözüm yöntemleri mevcuttur. Ancak son yıllarda özellikle doğal süreçlerden esinlenilmiş birçok optimizasyon algoritması geliştirilmiştir. Doğadaki birçok canlı sahip oldukları kusursuz tasarımlarıyla var olan kaynakları minimum kullanarak, maksimum performans elde etmeyi başararak en zorlu şartlarda bile hayatta kalmayı başarmıştır. Canlıların koordineli hareket ederek özellikle yiyecek bulmada gösterdikleri zeka, “sürü zekası”(swarm intelligence) olarak adlandırılmaktadır. Bu çalışmada özellikle son yıllarda geliştirilen ve doğadan esinlenen optimizasyon algoritmaları, bu algoritmaların uygulama alanları ve performans analizleri incelenmiştir. 

Keywords

• Sezgisel Araştırma
• Doğadan Esinlenen Algoritmalar
• Optimizasyon

Nature Inspired Optimization Algorithms and Optimization of the Optimization Algorithms

Abstract

Optimization is defined as a process of searching the best solution of a function which has one or more variable under some constraints. There are classical solution methods and heuristic optimization algorithms in order to solve optimization problems. Recently, it has been developed a lot of optimization algorithms inspired from nature. Most of the living being in the nature has been successful to survive perfect designs accomplishing to take maximum performance from minimum resource. The cooperative behavior of animals for finding foods is called swarm intelligence. In this study, recently developed optimization algorithms especially inspired from nature have been researched. It has been shown that for these algorithms in order to give optimum results is dependendent to the parameters used in the algorithms. 

Keywords

• Heuristic Search
• Nature-Inspired Algorithms
• Optimization

References

1. J.H Holland Adaptation in Natural and Artificial Systems University of Michigan Press, Ann Arbor, Michigan (1975)
2. D.E Goldberg Genetic Algorithms in Search, Optimization, and Machine Learning Addison-Wesley, Reading, MA (1989)
3. http://apps.webofknowladge.com/UA_GeneralSearch_input.do?product=UA&SID=N2j2zzcxZelCvVdCRdO&search_mode=GeneralSearch.( Son Erişim tarihi:03.01.2016)
4. S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi Optimization By Simulated Annealing Science 220( 4598) (1983) 671-680.
5. C. Blum Ant colony optimization: Introduction and recent trends Physics of Life Reviews 2(4)(2005) 353-373.
6. M. Dorigo Optimization Learning And Natural Algorithms Ph.D. Thesis, Politecnico Di Milano, Italy, (1992).
7. M. Dorigo, C. Blum, Ant colony optimization theory: A survey Theoretical Computer Science, 344(2–3)( 2005) 243-278.
8. K. Socha M. Dorigo Ant colony optimization for continuous domains European Journal of Operational Research 185(3)(2008)1155-1173.

9. J. Kennedy, R. Eberhart Particle swarm optimization IEEE Conference: 1995 IEEE International Conference on Neural Networks(ICNN95) , PROCEEDINGS, (1-6) ( 1942-1948 (1995)
10. D. Karaboga, B. Basturk On the Performance of Artificial Bee Colony (ABC) Algorithm, Applied Soft Computing 8 (1) (2008) 687–697.
11. C. Öztürk, E. Hançer, D. Karaboğa(2014) doi:http://dx.doi.org/10.17341/gummfd.00459
12. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO_files/
13. Page422.htm. (Erişim Tarihi:04.01.2016)
14. A. Yurtkuran, E. Emel(2015) http://dx.doi.org/10.1016/j.amc.2015.09.064.
15. Y. Wenchao, Z.Yinzhi, G. Liang, L. Xinyu, M. Jianhui(2016) http://dx.doi.org/10.1016/j.eswa.2015.09.031.
16. A.N.K. Nasir, M.O. Tokhi, N.M.A. Ghani(2015) http://dx.doi.org/10.1016/j.eswa.2014.09.010.
17. M. Aziz, M. Tayarani (2014) http://dx.doi.org/10.1016/j.engappai.2014.07.021.
18. L. Wang, H. Ni, R. Yang, P. M. Pardalos, X. Du, M. Fei(2015) http://dx.doi.org/10.1016/j.ins.2015.05.022.
19. X. Geng, Z. Chen, W. Yang, D. Shi, K. Zhao (2011) http://dx.doi.org/10.1016/j.asoc.2011.01.039.
20. A. H. Karami, M. Hasanzadeh(2015) http://dx.doi.org/10.1016/j.compeleceng.2014.12.014.
21. M. K. Naik, R. P.(2016) http://dx.doi.org/10.1016/j.asoc.2015.10.039.
22. T. Zhang, T. Hu, X. Guo, Z. Chen, Y. Zheng (2013) http://dx.doi.org/10.1016/j.knosys.2013.07.015.
23. S. Sarafrazi, H. Nezamabadi-pour, S. Saryazdi (2011) http://dx.doi.org/10.1016/j.scient.2011.04.003.
24. Z. Li, W. Wang, Y. Yan, Z. Li (2015) http://dx.doi.org/10.1016/j.eswa.2015.07.043.
25. H. Garg (2016) http://dx.doi.org/10.1016/j.amc.2015.11.001.
26. M. Toksari(2016) http://dx.doi.org/10.1016/j.ijepes.2015.12.032.
27. S. Sarafrazi, H. Nezamabadi-pour, S. R. Seydnejad (2015) http://dx.doi.org/10.1016/j.jksuci.2014.10.003.
28. M. Metlicka, D. Davendra (2015) http://dx.doi.org/10.1016/j.swevo.2015.03.002.
29. M. Ghasemi, S. Ghavidel, E. Akbari, A.A. Vahed(2014) http://dx.doi.org/10.1016/j.energy.2014.06.026.
30. X. Yuan, J. Zhao, Y.Yang, Y. Wang (2014) http://dx.doi.org/10.1016/j.asoc.2013.12.016.
31. M. Pluhacek, R. Senkerik, D. Davendra (2015)http://dx.doi.org/10.1016/j.swevo.2015.10.008.
32. M. Mitić, N. Vuković, M. Petrović, Z. Miljković(2015) http://dx.doi.org/10.1016/j.knosys.2015.08.010.
33. A. Askarzadeh (2013) http://dx.doi.org/10.1016/j.solener.2013.08.014.
34. H. Aguiar, O. Junior, M. A. S. Machado(2015) http://dx.doi.org/10.1016/j.procs.2015.07.002.
35. E. Amiri, S. Mahmoudi(2015) http://dx.doi.org/10.1016/j.asoc.2015.12.008.
36. F. Gaxiola, P. Melin, F. Valdez, J. R. C., O. Castillo(2016) http://dx.doi.org/10.1016/j.asoc.2015.10.027.
37. S.Shadmand, B. Mashoufi (2016) http://dx.doi.org/10.1016/j.bspc.2015.10.008.
38. R.J. Kuo, W.L. Tseng, F.C. Tien, T. W. Liao(2012) http://dx.doi.org/10.1016/j.cie.2012.06.006.
39. Z. Izakian, M. S. Mesgari, A. Abraham(2016) http://dx.doi.org/10.1016/j.compenvurbsys.2015.10.009.
40. H. Li, H. He, Y. Wen(2015) http://dx.doi.org/10.1016/j.ijleo.2015.09.127.
41. C. Ozturk, E. Hancer, D. Karaboga(2015) http://dx.doi.org/10.1016/j.asoc.2014.11.040.
42. B. Jiang, F. Qiu, L. Wang, Z. Zhang(2013) http://dx.doi.org/10.1016/j.ipm.2015.11.003.
43. J. Tvrdík, I. Křivý,(2015) http://dx.doi.org/10.1016/j.asoc.2015.06.032.
44. Y. Ding, X. Fu(2015) http://dx.doi.org/10.1016/j.neucom.2015.01.106.
45. S. Alam, G. Dobbie, S.U. Rehman(2015) http://dx.doi.org/10.1016/j.swevo.2015.10.003.
46. T.O. Ting, J. Ma, K. S. Kim, K. Huang(2016) http://dx.doi.org/10.1016/j.asoc.2015.10.054.
47. C. Wang, D. Mu, F. Zhao, J. W. Sutherland(2015) http://dx.doi.org/10.1016/j.cie.2015.02.005.
48. P. Cai, Y. Cai, I. Chandrasekaran, J. Zheng(2016) http://dx.doi.org/10.1016/j.autcon.2015.09.007.
49. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi(2009) http://dx.doi.org/10.1016/j.ins.2009.03.004.
50. M. Abdechiri, M. R. Meybodi, H. Bahrami(2013) http://dx.doi.org/10.1016/j.asoc.2012.03.068.
51. V. K. Patel, V. J. Savsani(2015) http://dx.doi.org/10.1016/j.ins.2015.06.044.
52. H. Abedinpourshotorban, S. M. Shamsuddin, Z. Beheshti, D. N.A. Jawawi (2015) http://dx.doi.org/10.1016/j.swevo.2015.07.002.
53. A. H. Kashan(2015) http://dx.doi.org/10.1016/j.cor.2014.10.011.
54. A. Baykasoğlu, Ş. Akpinar(2015) http://dx.doi.org/10.1016/j.asoc.2015.10.036.
55. A. Baykasoğlu, Ş. Akpinar(2015) http://dx.doi.org/10.1016/j.asoc.2015.08.052.
56. M.Ghaemi, M.R.Feizi-Derakhshi(2014) http://dx.doi.org/10.1016/j.eswa.2014.05.009.
57. I. Rbouh, A. Ameur El Imrani(2014) http://dx.doi.org/10.1016/j.aasri.2014.05.005.
58. H. Shah-Hosseini(2012) http://dx.doi.org/10.1016/j.sbspro.2012.01.033.
59. A. Hatamlou(2013) http://dx.doi.org/10.1016/j.ins.2012.08.023.
60. H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi(2012) http://dx.doi.org/10.1016/j.compstruc.2012.07.010.
61. H. Shareef, A. Asrul Ibrahim, A. Hussein Mutlag(2015) http://dx.doi.org/10.1016/j.asoc.2015.07.028.
62. M. Dorigo, V. Maniezzo, A Colorni(1996) doi: 10.1109/3477.484436
63. T. Stutzle, H.H. Hoos MAX–MIN Ant System Future Generation Computer Systems 16(8)(2000) 889–914.
64. M. Clerc, M; Kennedy, J. (2002) doi: 10.1109/4235.985692
65. D. Karaboga, B. Basturk A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm JOURNAL OF GLOBAL OPTIMIZATION 39(3)(2007)
66. W. Pan (2012) http://dx.doi.org/10.1016/j.knosys.2011.07.001.
67. X. S. Yang, S. Deb (2009) doi: 10.1109/NABIC.2009.5393690
68. A. H. Gandomi, A. H. Alavi (2012) doi:10.1016/j.cnsns.2012.05.010
69. K.M. Passino(2002) doi: 10.1109/MCS.2002.1004010
70. X. S. Yang A New Metaheuristic Bat-Inspired Algorithm International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)Location: Tenerife, SPAIN Date: 2008
71. X.S. Yang, Firefly Algorithms for Multimodal Optimization, Lecture Notes in Computer Science 5792(2009) 169-178
72. B.R. Rajakumar, The Lion's Algorithm: A New Nature-Inspired Search Algorithm, Procedia Technology 6(2012) 126-135
73. S. Mirjalili, S. M. Mirjalili, A. Lewis (2014)http://dx.doi.org/10.1016/j.advengsoft.2013.12.007.
74. A. Kaveh, N. Farhoudi(2013) http://dx.doi.org/10.1016/j.advengsoft.2013.03.004.
75. A.R. Mehrabian, C. Lucas(2006) http://dx.doi.org/10.1016/j.ecoinf.2006.07.003.
76. S.A. Uymaz, G. Tezel, E. Yel(2015) http://dx.doi.org/10.1016/j.asoc.2015.03.003.
77. M. D. Li, H. Zhao, X. W. Weng, T. Han(2016) http://dx.doi.org/10.1016/j.advengsoft.2015.11.004.
78. O. Abedinia, N. Amjady,A.Ghasemi(2014) DOI: 10.1002/cplx.21634
79. J. J.Q. Yu, V. O.K. Li(2015) http://dx.doi.org/10.1016/j.asoc.2015.02.014
80. L. Wang, H. Ni, R. Yang, P. M. Pardalos, X. Du, M. Fei(2015) http://dx.doi.org/10.1016/j.ins.2015.05.022.