Dinamik Çok Amaçlı Eniyileme Problemleri için Hibrid Çerçevenin İncelenmesi

Çok amaçlı evrimsel algoritmalar ve sezgisel seçen üst-sezgiseller ortamda meydana gelebilecek farklı dinamizm tiplerini ele alan adaptif yöntemlerdir. Bu çalışmada, bu yöntemlerin birleştirildiği yapı, dinamik çok amaçlı eniyileme problemlerini çözmek için kullanılmıştır. Bu yapıda üst-sezgiseller toplumun bireylerini üretecek olan sezgiselleri seçmek için kullanılır. Sezgisel seçen üst-sezgiseller içinde kullanılan farklı sezgisel seçim yöntemlerinin etkisi ile birlikte önerilen yaklaşımın performansı yapay olarak oluşturulmuş dinamik test problemleri üzerinde deneysel olarak incelenmiştir. Deneysel sonuçlar öğrenme içeren üst-sezgisellerin kullanıldığı yaklaşımın öğrenme içermeyenlere göre daha iyi sonuç verdiğini göstermiştir. Ayrıca, önerilen yaklaşımın literatürde iyi bilinen yöntemlerle karşılaştırıldığında rekabet edebilecek düzeyde sonuçlar verdiği görülmüştür.

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  • Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., ve Qu, R., "Hyper-heuristics: a survey of the state of the art", Journal of the Operational Research Society, Cilt 64, No 12, 1695-1724, 2013.
  • [2] Özcan, E., Bilgin, B., ve Korkmaz, E.E., "A comprehensive analysis of hyper-heuristics", Intell. Data Anal., Cilt 12, No 1, 3-23, 2008.
  • [3] Cowling, P.I., Kendall, G., ve Soubeiga, E., "A Hyperheuristic Approach to Scheduling a Sales Summit". Proc. Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III, 176-190, 2001.
  • [4] McClymont, K., Keedwell, E., Savić, D., ve Randall-Smith, M., "A general multi-objective hyper-heuristic for water distribution network design with discolouration risk", Journal of Hydroinformatics, Cilt 15, No 3, 700-716, 2013.
  • [5] Kiraz, B., Etaner-Uyar, A.Ş., ve Özcan, E., "Selection hyper-heuristics in dynamic environments", Journal of the Operational Research Society, Cilt 64, No 12, 1753-1769, 2013.
  • [6] Deb, K., Pratap, A., Agarwal, S., ve Meyarivan, T., "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, Cilt 6, No 2, 182-197, 2002.
  • [7] Coello, C.A., "An updated survey of GA-based multiobjective optimization techniques", ACM Comput. Surv., Cilt 32, No 2, 109-143, 2000.
  • [8] Deb, K., "Multi-Objective Optimization Using Evolutionary Algorithms", John Wiley, 2001.
  • [9] Farina, M., Deb, K., ve Amato, P.: "Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications", Evolutionary Multi-Criterion Optimization: Second International Conference, EMO 2003, Faro, Portugal, April 8–11, 2003. Proceedings, in Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., ve Deb, K. (Ed.)^(Eds.), Springer Berlin Heidelberg, 311-326, 2003.
  • [10] Jin, Y., ve Branke, J., "Evolutionary optimization in uncertain environments-a survey", IEEE Transactions on Evolutionary Computation, Cilt 9, No 3, 303-317, 2005.
  • [11] Yang, S., ve Yao, X., "Evolutionary Computation for Dynamic Optimization Problems", 2013.
  • [12] Cobb, H.G., "An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments", Rep.No, Naval Research Lab., Washington, DC, 1990.
  • [13] Deb, K., Rao N., U.B., ve Karthik, S.: "Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling", Evolutionary Multi-Criterion Optimization: 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007. Proceedings, in Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., ve Murata, T. (Ed.)^(Eds.), Springer Berlin Heidelberg, 803-817, 2007.
  • [14] Uyar, A.Ş., ve Harmanci, A.E., "A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments", Soft Computing, Cilt 9, No 11, 803-814, 2005.
  • [15] Yang, S., ve Yao, X., "Population-Based Incremental Learning With Associative Memory for Dynamic Environments", IEEE Transactions on Evolutionary Computation, Cilt 12, No 5, 542-561, 2008.
  • [16] Yang, S., "Genetic algorithms with memory-and elitism-based immigrants in dynamic environments", Evol. Comput., Cilt 16, No 3, 385-416, 2008.
  • [17] Wang, Y., ve Li, B., "Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment". Proc. 2009 IEEE Congress on Evolutionary Computation, 630-637, 18-21 May 2009, 2009.
  • [18] Branke, J., "Evolutionary Optimization in Dynamic Environments", Kluwer Academic Publishers, 2001.
  • [19] Helbig, M., "Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation", University of Pretoria, Faculty of Engineering, Built Environment and Information Technology, 2012.
  • [20] Goh, C.-K., ve Tan, K.C., "A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization", Trans. Evol. Comp, Cilt 13, No 1, 103-127, 2009.
  • Sahmoud, S., ve Topcuoglu, H.R., "A Memory-Based {NSGA-II} Algorithm for Dynamic Multi-objective Optimization Problems". Proc. 19th European Conference, EvoApplications 2016, Porto, Portugal, 296--310, 2016.
  • [22] Helbig, M., Deb, K., ve Engelbrecht, A.P., "Key challenges and future directions of dynamic multi-objective optimisation". Proc. {IEEE} Congress on Evolutionary Computation, Vancouver, BC, Canada, 1256--1261, 2016.
  • [23] Nareyek, A.: "Choosing Search Heuristics by Non-Stationary Reinforcement Learning", Metaheuristics: Computer Decision-Making, in (Ed.)^(Eds.), Springer US, 523-544, 2004.
  • [24] Ozcan, E., Uyar, S.E., ve Burke, E., "A greedy hyper-heuristic in dynamic environments". Proc. Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, Montreal, Canada, 2201-2204, 2009.
  • [25] Uludağ, G., Kiraz, B., Etaner-Uyar, A.Ş., ve Özcan, E., "A hybrid multi-population framework for dynamic environments combining online and offline learning", Soft Computing, Cilt 17, No 12, 2327-2348, 2013.
  • [26] Topcuoglu, H.R., Ucar, A., ve Altin, L., "A hyper-heuristic based framework for dynamic optimization problems", Applied Soft Computing, Cilt 19, No, 236-251, 2014.
  • [27] Burke, E.K., Silva, J.D.L., ve Soubeiga, E.: "Multi-Objective Hyper-Heuristic Approaches for Space Allocation and Timetabling", Metaheuristics: Progress as Real Problem Solvers, in Ibaraki, T., Nonobe, K., ve Yagiura, M. (Ed.)^(Eds.), Springer US, 129-158, 2005.
  • [28] McClymont, K., ve Keedwell, E.C., "Markov chain hyper-heuristic (MCHH): an online selective hyper-heuristic for multi-objective continuous problems". Proc. Proceedings of the 13th annual conference on Genetic and evolutionary computation, Dublin, Ireland, 2003-2010, 2011.
  • [29] Zitzler, E., Laumanns, M., Thiele, L., "SPEA2: Improving the Performance of the Strength Pareto Evolutionary Algorithm", Rep.No: 103, Swiss Federal Institute of Technology (ETH) Zurich 2001.
  • [30] Gomez, J.C., ve Terashima-Marín, H.: "Approximating Multi-Objective Hyper-Heuristics for Solving 2D Irregular Cutting Stock Problems", Advances in Soft Computing: 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Pachuca, Mexico, November 8-13, 2010, Proceedings, Part II, in Sidorov, G., Hernández Aguirre, A., ve Reyes García, C.A. (Ed.)^(Eds.), Springer Berlin Heidelberg, 349-360, 2010.
  • Kumari, A.C., Srinivas, K., ve Gupta, M.P., "Software module clustering using a hyper-heuristic based multi-objective genetic algorithm". Proc. 2013 3rd IEEE International Advance Computing Conference (IACC), 813-818, 22-23 Feb. 2013, 2013.
  • [32] Suganthan, P.N., "Performance assessment on multi-objective optimization algorithms". Proc. IEEE Conference on Evolutionary Computation Special Session-competition on performance assessment of multi-objective optimization algorithms, 2007.
  • [33] Das, S., ve Suganthan, P.N., "Differential Evolution: A Survey of the State-of-the-Art", IEEE Transactions on Evolutionary Computation, Cilt 15, No 1, 4-31, 2011.
  • [34] Tan, K.C., Lee, T.H., ve Khor, E.F., "Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons", Artificial Intelligence Review, Cilt 17, No 4, 251-290, 2002.
  • [35] Özcan, E., Misir, M., Ochoa, G., ve Burke, E.K., "A Reinforcement Learning-Great-Deluge Hyper-Heuristic for Examination Timetabling", Int. J. Appl. Metaheuristic Comput., Cilt 1, No 1, 39-59, 2010.
  • [36] Zhang, Q., Zhou, A., ve Jin, Y., "RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm", IEEE Transactions on Evolutionary Computation, Cilt 12, No 1, 41-63, 2008.
  • [37] Koo, W.T., Goh, C.K., ve Tan, K.C., "A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment", Memetic Computing, Cilt 2, No 2, 87-110, 2010.
  • [38] Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., ve Tsang, E., "Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization". Proc. Proceedings of the 4th international conference on Evolutionary multi-criterion optimization, Matsushima, Japan, 832-846, 2007.
Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji-Cover
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2013
  • Yayıncı: Gazi Üniversitesi , Fen Bilimleri Enstitüsü