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SETSCI - Volume 1 (2017)
ISMSIT2017 - International Symposium on Multidisciplinary Studies and Innovative Technologies, Tokat, Turkey, Dec 02, 2017

Evrimsel Algoritma ile Parçacık Sürü Algoritmasının Simülasyon Tabanlı Karşılaştırılması ve Elde Edilen Sonuçların Analizi
Serkan Dereli1, Raşit Köker2, İsmail Öylek3*, Metin Varan4
1Sakarya University, Sakarya, Turkey
2Sakarya University, Sakarya, Turkey
3Sakarya University, Sakarya, Turkey
4Sakarya University, Sakarya, Turkey
* Corresponding author: ioylek@sakarya.edu.tr
Published Date: 2017-12-08   |   Page (s): 252-255   |    477     5
ABSTRACT Evrimsel algoritma ve parçacık sürü algoritması çok uzun zamandır ayrı ayrı çeşitli karmaşık problemlerin çözümünde yaygın olarak kullanılan sezgisel algoritmalardır. Evrimsel algoritma canlıların çoğalmasını ve hayatta kalmak için en iyilerin seçilmesi tekniğine dayanırken, parçacık sürü algoritması kuş ve balık sürülerinin yiyecek arama davranışlarından esinlenilerek geliştirilmiştir. Her iki algoritmanın da üstün olduğu taraflar vardır. Parçacık sürü algoritması iyi sonuçlara daha iyi yaklaşırken, evrimsel algoritma ise iyilerin yeni nesillere aktarılması ve adaptasyon neticesinde daha kısa çalışma zamanına sahiptir. İşte bu çalışmada konu edilen algoritmaların sahip olduğu bu üstünlüklerin simülasyon tabanlı bir uygulama ile açıkça gösterilmektedir.  

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Evolutionary algorithm and particle swarm algorithm are heuristic algorithms that have been widely used to solve various complex problems for a very long time. While the evolutionary algorithm is based on the idea of increasing living things and choosing the best ones for survival, the particle swarm algorithm has been developed by inspiring food search behaviors of birds and fish. There are sides where both algorithms are superior. While the particle swarm algorithm better approximates the results, the evolutionary algorithm has a shorter working time due to the transfer of goodness to the next generation and adaptation. In this work, it is a study in which the advantages of the algorithms are clearly demonstrated by a simulation-based application.
KEYWORDS evolutionary algorithm, particle swarm algorithm, optimization, heuristic method
REFERENCES [1] S. Dereli, R. Köker, “Design and Analysis of Multi-Layer Artificial Neural Network Used for Training in Inverse Kinematic Solution of 7- DOF Serial Robot,” Gaziosmanpasa Journal of Scientific Research, vol. 6, pp. 60-71, 2017.

[2] A.P.Engelbrecht. Computational Intelligence. London: Wiley, 2007.

[3] Yang, X.S. Engineering Optimization. New Jersey: Wiley, 2010.

[4] M.El-Abd, “Performance assessment of foraging algorithms vs. evolutionary algorithms,” Information Sciences, vol. 182, pp. 243- 263, 2010.

[5] M.Fadee, M.A.M.Radzi, “Multi-objective optimization of a stand alone hybrid renewable energy system: A review,” Renewable and Sustainable Energy Reviews, vol. 16, pp. 3364-3369, 2012.

[6] A.Zhou, B.Y.Qu, P.N.Suganthan, Q.Zhang, “Multiobjective evolutionary algorithms: A survey of the state of the art,” Swarm and Evolutionary Computation, vol. 1, pp. 32-49, 2011.

[7] G.Zhang, “Quantum-inspired evolutionary algorithms: a survey and empirical study,” Journal of Heuristics, vol. 17, pp. 303-351, 2011.

[8] A.Lara, G.Sanchez, C.A.Coello, O.Schütze, “HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms,” IEEE Transactions on Evolutionary Computation, vol. 14, pp. 112-132, 2010.

[9] A.Mukhopadhyay, U.Maulik, S.Bandyopadhyay, C.A.Coello, “A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I,” IEE Transactions On Evolutionary Computation, vol. 18, pp. 4-19, 2014.

[10] A.Martens, H.Koziolek, S.Becker, R.Reussner, “Automatically Improve Software Architecture Models for Performance, Reliability, and Cost Using Evolutionary Algorithms,” in Proc International Conference on Performance Engineering, 2010.

[11] M.Crepinsek, S.H.Liu, M.Mernik, “Exploration and Exploitation in Evolutionary Algorithms: A Survey,” ACM Computing Surveys, vol. 45, pp. 1-27, 2013.

[12] S.Dereli, R.Köker, “IW-PSO approach to the inverse kinematics problem solution of a 7-Dof serial robot manipulator,” Sigma Journal of Engineering and Natural Sciences, vol. 35, pp. , 2017.

[13] M.Sharafia, T.Y.ELMekkawy, “Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach,” Renewable Energy, vol. 68, pp. 67-79, 2014.

[14] Y.Wang, J.Lv, L.Zhu, Y.Ma, “Crystal Structure Prediction via Particle Swarm Optimization,” Physical Review, 2010.

[15] J.C.Bansal, P.K.Singh, M.Saraswat, A.Verma, S.S.Jadon, A.Abraham, “Inertia Weight Strategies in Particle Swarm Optimization,” in Proc. IEEE Nature and Biologically Inspired Computing (NaBIC), 2011.

[16] D.Lim, Y.Jin, Y.S.Ong, B.Sendhoff, “Generalizing Surrogate-Assisted Evolutionary Computation,” IEEE Transactions on Evolutionary Computation, vol. 14, pp. 329-355, 2009.

[17] A.Kusiak, H.Zheng, “Optimization of wind turbine energy and power factor with an evolutionary computation algorithm,” Energy, vol. 35, pp. 1324-1332, 2010.

[18] J.Kennedy, R.C.Eberhart, “Particle Swarm Optimization,” in Proc. EEE international conference on neural network, 1995.

[19] R.Poli, J.Kennedy, T.Blackwell, “Particle Swarm Optimization,” Swarm Intelligence, vol. 1, pp. 33-57, 2007.

[20] S.S.Chiddarwar, N.R.Babu, “Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach,” Enginering Applications of Artificial Intelligence, vol. 23, pp. 1083-1092, 2010.

[21] R.Köker, “A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization,” Information Science, vol. 222, pp. 528-543, 2013.

[22] T.X.Yen, D.Sharma, D.Sirinivasan, P.N.Manji, “A modified particle swarm optimization approach for unit commitment,” in Proc IEEE Evolutionary Computation (CEC), 2011.


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