Kısıtlı optimizasyon problemlerinin çözümü için atom arama optimizasyon algoritması

Yıl 2019, Cilt: 10 Sayı: 3, 841 - 851, 29.09.2019

Erdal Eker , Murat Kayri , Serdar Ekinci

https://doi.org/10.24012/dumf.540402

Cited By: 1

Öz

Son
yıllarda evrim, fizik, matematik ve sürü ilhamlı çok sayıdaki sezgisel-üstü
optimizasyon teknikleri, bilim ve mühendislik alanlarına önerildi. Atom arama
optimizasyonu (ASO), temel moleküler dinamiklerden esinlenen popülasyon tabanlı
yeni bir optimizasyon algoritmasıdır. ASO, basitliği ve az sayıda kontrol
parametresi sayesinde optimizasyon problemlerine kolaylıkla uygulanabilir. ASO,
en çok bilinen sekiz test fonksiyonuna (Sphere, Rosenbrock, Step, Schwefel,
Rastrigin, Ackley, Griewank ve Egg Crate) uygulandı. Ayrıca, her test
fonksiyonu için ASO ile elde edilen istatistiksel sonuçlar (ortalama, standart
sapma ve en iyi değer) literatürdeki diğer algoritmalarla elde edilen
sonuçlarla karşılaştırıldı. Parçacık sürüsü optimizasyonu (PSO), yapay arı
kolonisi (ABC) ve sinüs kosinüs algoritması (SCA) karşılaştırma için seçilen diğer
metotlardır. Tüm test fonksiyonları için elde edilen istatistiksel sonuçlar ve
yakınsama hızlarına bakıldığında, ASO algoritmasının kısıtlı optimizasyon
problemlerini çözmedeki üstün performansı göze çarpmaktadır.  

Anahtar Kelimeler

Atom arama optimizasyonu, sezgisel-üstü, optimizasyon, test fonksiyonları

Kaynakça

• Basturk, B., Karaboga, D., (2006). An artificial bee colony (ABC) algorithm for numeric function optimization, IEEE Swarm Intelligence Symposium, Indianapolis.
• Boussaïd, I., Lepagnot, J., Siarry, P., (2013). A survey on optimization metaheuristics, Information sciences, 237, 82-117.
• Eberhart, R., Kennedy, J., (1995). A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, Nagoya.
• Ekinci, S., (2016). Application and comparative performance analysis of PSO and ABC algorithms for optimal design of multi-machine power system stabilizers, Gazi University Journal of Science, 29(2), 323-334.
• Ekinci, S., Demiroren, A., (2016). Modeling, simulation, and optimal design of power system stabilizers using ABC algorithm, Turkish Journal of Electrical Engineering & Computer Sciences, 24(3), 1532-1546.
• Ekinci, S., Hekimoglu, B., (2017). Multi-machine power system stabilizer design via HPA algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 32(4), 1271-1285.
• Ekinci S., (2019). Optimal design of power system stabilizer using sine cosine algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, https://dx.doi.org/10.17341/gazimmfd.460529.
• Gandomi, A.H., Alavi, A.H., (2012). Krill herd: a new bio-inspired optimization algorithm, Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845.
• Hekimoğlu, B., (2019). Sine-cosine algorithm-based optimization for automatic voltage regulator system. Transactions of the Institute of Measurement and Control, to be published. DOI: 10.1177/0142331218811453.
• Holland, J.H., (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor.
• Jaddi, N.S., Alvankarian, J., Abdullah, S., (2017). Kidney-inspired algorithm for optimization problems, Communications in Nonlinear Science and Numerical Simulation, 42, 358-369.
• Jain, M., Singh, V., Rani, A., (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm, Swarm and Evolutionary Computation, 44, 148-175.
• Karaboga, D., Artificial Bee Colony (ABC) Algorithm, https://abc.erciyes.edu.tr, Erişim tarihi Şubat 16, 2019.
• Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., (1983). Optimization by simulated annealing, Science, 220(4598), 671-680.
• Li, M.D., Zhao, H., Weng, X.W., Han, T., (2016). A novel nature-inspired algorithm for optimization: Virus colony search, Advances in Engineering Software, 92, 65-88.
• Mirjalili, S., (2015). The ant lion optimizer, Advances in Engineering Software, 83, 80-98.
• Mirjalili, S., (2016a). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27(4), 1053-1073.
• Mirjalili, S., (2016b). SCA: a sine cosine algorithm for solving optimization problems, Knowledge-Based Systems, 96, 120-133.
• Mirjalili, S., Lewis, A., (2016). The whale optimization algorithm, Advances in Engineering Software, 95, 51-67.
• Mirjalili, S., Sine Cosine Algorithm (SCA), http://www.alimirjalili.com/SCA.html, Erişim tarihi Şubat 18, 2019.
• Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., (2009). GSA: a gravitational search algorithm, Information Sciences, 179(13), 2232-2248.
• Saremi, S., Mirjalili, S., Lewis, A., (2017). Grasshopper optimisation algorithm: theory and application, Advances in Engineering Software, 105, 30-47.
• Yang, X.S., (2009). Firefly algorithms for multimodal optimization, International Symposium on Stochastic Algorithms, Springer, Heidelberg.
• Yang, X.S., (2010). A new metaheuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, Heidelberg.
• Yang, X.S., (2012). Flower pollination algorithm for global optimization, International Conference on Unconventional Computing and Natural Computation, Springer, Heidelberg.
• Yarpiz Team, Particle Swarm Optimization (PSO), https://www.mathworks.com/matlabcentral/fileexchange/52857-particle-swarm-optimization-pso , Erişim tarihi Şubat 17, 2019.
• Zhao, W., Atom Search Optimization (ASO) Algorithm, https://www.mathworks.com/matlabcentral/fileexchange/67011-atom-search-optimization-aso-algorithm, Erişim tarihi Şubat 21, 2019.
• Zhao, W., Wang, L., Zhang, Z., (2019a). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem, Knowledge-Based Systems, 163, 283-304.
• Zhao, W., Wang, L., Zhang, Z., (2019b). A novel atom search optimization for dispersion coefficient estimation in groundwater, Future Generation Computer Systems, 91, 601-610.