Research Article
Open Access
Evrimsel Algoritma ile Parçacık Sürü Algoritmasının Simülasyon Tabanlı Karşılaştırılması ve Elde Edilen Sonuçların Analizi
1Sakarya University
✉ Corresponding author: İsmail Öylek · ioylek@sakarya.edu.tr
Proceedings International Symposium on Multidisciplinary Studies and Innovative Technologies
Volume 1 Pages 252–255 Year 2017
References
- 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.
- A.P.Engelbrecht. Computational Intelligence. London: Wiley, 2007.
- Yang, X.S. Engineering Optimization. New Jersey: Wiley, 2010.
- M.El-Abd, “Performance assessment of foraging algorithms vs. evolutionary algorithms,” Information Sciences, vol. 182, pp. 243- 263, 2010.
- 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.
- 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.
- G.Zhang, “Quantum-inspired evolutionary algorithms: a survey and empirical study,” Journal of Heuristics, vol. 17, pp. 303-351, 2011.
- 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.
- 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.
- 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.
- M.Crepinsek, S.H.Liu, M.Mernik, “Exploration and Exploitation in Evolutionary Algorithms: A Survey,” ACM Computing Surveys, vol. 45, pp. 1-27, 2013.
- 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.
- 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.
- Y.Wang, J.Lv, L.Zhu, Y.Ma, “Crystal Structure Prediction via Particle Swarm Optimization,” Physical Review, 2010.
- 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.
- 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.
- A.Kusiak, H.Zheng, “Optimization of wind turbine energy and power factor with an evolutionary computation algorithm,” Energy, vol. 35, pp. 1324-1332, 2010.
- J.Kennedy, R.C.Eberhart, “Particle Swarm Optimization,” in Proc. EEE international conference on neural network, 1995.
- R.Poli, J.Kennedy, T.Blackwell, “Particle Swarm Optimization,” Swarm Intelligence, vol. 1, pp. 33-57, 2007.
- 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.
- 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.
- 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.