American Journal of Information Science and Computer Engineering
Articles Information
American Journal of Information Science and Computer Engineering, Vol.1, No.3, Sep. 2015, Pub. Date: Jul. 9, 2015
Physics Based Metaheuristic Algorithms for Global Optimization
Pages: 94-106 Views: 1246 Downloads: 1930
[01] Umit Can, Tunceli University, Department of Computer Engineering, Tunceli, Turkey.
[02] Bilal Alatas, Firat University, Department of Software Engineering, Elazig, Turkey.
In recent years, several optimization methods especially metaheuristic optimization methods have been developed by scientists. People have utilized power of nature to solve problems. Therefore, those metaheuristic methods have imitated physical and biological processes of nature. In 2007, Big Bang Big Crunch optimization algorithm based on evolution of universe and in 2009, Gravitational Search Algorithm based on gravity law have been proposed and have been applied to solve complex problems. However, there have been proposed many physics based algorithms afterwards. Although, many of the proposed metaheuristic optimization algorithms are known as biology based, in fact, the number of the proposed physics based metaheuristic algorithms is not less than that of algorithms based on biology. In this paper; all of the current physics based metaheuristic optimization algorithms have been searched, collected, and introduced with the performed studies.
Metaheuristic Optimization, Physics Based Metaheuristic Optimization, Artificial Intelligence Optimization Algorithms
[01] Rashedi, E., Nezamabadi, H., Saryazdi, S., (2009). Gravitational Search Algorithm, Information Sciences, 179, 2232-2248.
[02] Duman, S. Sonmez, Y. Guvenc, U. Yorukeren, N., (2011). Application of Gravitational Search Algorithm for Optimal Reactive Power Dispatch Problem, 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Istanbul, 519-523.
[03] Rashedi, E. Nezamabadi-pour, H. Saryazdi, S. M.Farsangi, M., (2007). Allocation of Static Var Compensator Using Gravitational Search Algorithm, First Joint Congress on Fuzzy and Intelligent Systems Ferdowsi University of Mashhad, Iran
[04] Chatterjee, A. and Mahanti, G.K., (2010). Comparative Performance of Gravitational Search Algorithm and Modified Particle Swarm Optimization for Synthesis of Thinned Scanned Concentric Ring Array Antenna, Progress In Electromagnetics Research B, 25, 331-348.
[05] Duman, S. Guvenc, U. Yorukeren, N., (2010). Gravitational Search Algorithm for Economic Dispatch with Valve-Point Effects, International Review of Electrical Engineering (I.R.E.E.), 5, 6
[06] Birbil, S.I., Fang, S.C., (2003). An Electromagnetism-like Mechanism for Global Optimization Journal of Global Optimization 25, 263-282.
[07] Wang, X.Y. Gao, L. Zhang, C.Y., (2008). Electromagnetism-Like Mechanism Based Algorithm for Neural Network Training, Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence Lecture Notes in Computer Science, Volume 5227/2008, 40-45.
[08] Abdullah, S. Turabeih, H. McCollum, B., (2009). Electromagnetism-like Mechanism with Force Decay Rate Great Deluge for the Course Timetabling Problem, Lecture Notes in Computer Science, Volume 5589/2009, 497-504.
[09] Ana Maria A.C. Rocha, Edite M.G.P. Fernandes, (2007). A new Electromagnetism-like Algorithm with a Population Shrinking Strategy, 6th WSEAS International Conference on System Science and Simulation in Engineering, Venice, 21-23.
[10] Lee, C.H. Chang, F.K. Lee, Y.C., (2010). An Improved Electromagnetism-like Algorithm for Recurrent Neural Fuzzy Controller Design, International Journal of Fuzzy Systems, Vol. 12, 280-290.
[11] Formato, R. A., (2007). Central force optimization: a new metaheuristic with applications in applied electromagnetics, Progress In Electronagnetics Research, PIER 77, 425-491.
[12] Formato, R.A., (2010). Central Force Optimization Applied to the PBM Suite of Antenna, The Computing Research Repository, vol. 1003, 1-89.
[13] Asi, M. J. Dib, N. I. (2010). Design of multilayer microwave broadband absorbers using central force optimization, Progress In Electromagnetics Research B, Vol. 26, 101-113.
[14] Shah-Hosseini, H., (2009a). Optimization with the Nature-Inspired Intelligent Water Drops Algorithm, In W. P. Dos Santos (Eds.), Evolutionary computation, 297-320, Austria: I-Tech, Vienna.
[15] Shah-Hosseini, H., (2009b). The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, Int.J Bio-Inspired Computation, Vol. 1, No. 2
[16] Duan, H. Liu, S. Lei, X., (2008). Air robot path planning based on Intelligent Water Drops optimization, IEEE International Joint Conference on Neural Networks, Hong Kong, 1397-1401.
[17] Kamkar, I. Akbarzadeh-T, M.-R. Yaghoobi, M., (2010). Intelligent Water Drops: a new optimization algorithm for solving the Vehicle Routing Problem, 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), 4142-4146.
[18] Rabanal, P. Rodriguez, I. Rubio, F., (2008). Solving Dynamic TSP by using River Formation Dynamics, Fourth International Conference on Natural Computation, Jinan, 246-250.
[19] Rabanal, P. Rodriguez, I. Rubio, F., (2010). Applying River Formation Dynamics to the Steiner Tree Problem, 9th IEEE International Conference on Cognitive Informatics (ICCI), Beijing, 704-711.
[20] Hsiao, Y. T., Chuang, C., L., Jiang, J. A., Chien, C. C., (2005). A Novel Optimization Algorithm: Space Gravitational Optimization, 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 3, 2323-2328.
[21] Wagner F. Sacco, Cassiano R.E. de Oliveria, (2005). A New Stochastic Optimization Algorithm based on a Particle Collision Metaheuristic, 6th World Congresses of Structural and Multidisciplinary Optimization. Rio de Janerio, Brazil
[22] Abuhamdah, A. Ayob, M., (2009). Experimental Result of Particle Collision Algorithm for Solving Course Timetabling Problems, IJCSNS International Journal of Computer Science and Network Security, vol 9, 134-142
[23] da Luz, E. F. P., Becceneri, J. C., de Campos Velho H., F., (2008). A new multi-particle collision algorithm for optimization in a high performance environment, Journal of Computational Interdisciplinary Sciences, 1(1): 3-10.
[24] Genç, H.M. Eksin, I. Erol, O.K., (2010). Big Bang - Big Crunch Optimization Algorithm Hybridized With Local Directional Moves and Application to Target Motion Analysis Problem, 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), 881 – 887.
[25] Genc, H.M. Hocaoglu, A.K., (2008). Bearing-Only Target Tracking Based on Big Bang – Big Crunch Algorithm, The Third International Multi-Conference on Computing in the Global Information Technology, Athens, 229-233.
[26] Alatas, B., (2011). Uniform Big Bang–Chaotic Big Crunch Optimization, Communication Nonlinear Science and Numerical Simulation, 16(9), 3696-3703.
[27] Jaradat, G.M. Ayob, M., (2010). Big Bang-Big Crunch Optimization Algorithm to Solve the Course Timetabling Problem, 10th International Conference on Intelligent Systems Design and Applications (ISDA), Cairo, 1448-1452.
[28] Shah-Hosseini, H. (2011a). Otsu’s Criterion-based Multilevel Thresholding by a Nature-inspired Metaheuristic called Galaxy-based Search Algorithm, 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC), Salamanca, 383-388.
[29] Shah-Hosseini, H., (2011b). Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation, Int. J. Computational Science and Engineering, Vol. 6, 132-140.
[30] Kripta, M. M.L. Kripta. R., (2008). Big Crunch Optimization Method, International Conference on Engineering Optimization, Rio de Janeiro
[31] Chuang, C. Cl., Jiang, J. A., (2007). Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space-time, IEEE Congress on Evolutionary Computation, Singapore, 3157-3164.
[32] Kaveh, A., Talatahari, S., (2010a). A novel heuristic optimization method: charged system Search, Acta Mechanica, Volume 213, 267-289
[33] Kaveh, A., Talatahari, (2010b). Charged system search for optimum grillage system design using the LRFD-AISC code, Journal of Constructional Steel Research, Volume 66, 767-771.
[34] Talatahari, S., Kaveh, A., (2011). Sheikholeslami, R., An efficient charged system search using chaos for global optimization problems, International Journal of Optimization in Civil Engineering, No. 2, 1, 305-325.
[35] Kaveh, A., Talatahari, (2010c). A Charged System Search with a Fly to Boundary Method for Discrete Optimum Design of Truss Structures, Asian Journal of Civil Engineering, Vol. 11, 277-293.
[36] Kaveh, A., Talatahari, (2012). Charged system search for optimal design of frame structures, Applied Soft Computing, Volume 12, January 2012, 382-393.
[37] Xie, L., Zeng J., Cui, Z., (2009). General framework of Artificial Physics Optimization Algorithm, World Congress on Nature & Biologically Inspired Computing, Coimbatore, 1321-1326.
[38] Xie, L., Zeng, J., Cui, Z., (2009). The Vector Model of Artificial Physics Optimization Algorithm for Global Optimization Problems, Intelligent Data Engineering and Automated Learning, Volume 5788, 610-617.
[39] Xie, LP, Zeng, JC, (2010). The Performance Analysis of Artificial Physics Optimization Algorithm Driven by Different Virtual Forces, ICIC Express Letters (ICIC-EL).
[40] Tayarani M. H., Akbarzadeh, M.R., (2008). Magnetic Optimization Algorithms a new synthesis, in IEEE Congress on Evolutionary Computation, 2659-2664.
[41] Mirjalili, S., Sadiq, A. S., (2011). Magnetic Optimization Algorithm for training Multi Layer Perceptron IEEE conference on Communication Software and Networks (ICCSN), 42-46.
[42] Mirjalili, S., Hashim, S. Z. M., (2012). BMOA: Binary Magnetic Optimization Algorithm, International Journal of Machine Learning and Computing, Vol. 2, No. 3, 204-208.
[43] Abidin, A. F. Z., (2012). Magnetic Optimization Algorithm Approach For Travelling Salesman Problem, World Academy of Science, Engineering and Technology 62, 1393-1397.
[44] Javidy B. et al., (2015). Ions motion algorithm for solving optimization problems, Applied Soft Computing, 32, 72-79.
[45] Zheng, M. Et al.,(2010). Gravitation Field Algorithm And Its Application in Gene Cluster, Algorithms for Molecular Biology, 5:32, 1-11.
[46] Rong, G., et al., (2013). Parallel Gravitation Field Algorithm Based on the CUDA Platform, Journal of Information & Computational Science, 10:12, 3635-3644.
[47] Flores, J. J., López, R., Barrera, J. (2011). Gravitational Interactions Optimization, Learning and Intelligent Optimization Lecture Notes in Computer Science, 6683, 226-237.
[48] Zaránd, G., Pázmándi, F., Pál, K. F., and Zimányi, G. T., (2002.) Using Hysteresis for Optimization, Phys. Rev. Lett. 89, 150201.
[49] Pál, K. F., (2003). Hysteretic optimization for the traveling salesman problem, Physica A: Statistical Mechanics and its Applications, Volume 329, Issues 1–2, 1 287–297.
[50] Pál, K. F., (2006). Hysteretic optimization, faster and simpler, Physica A: Statistical Mechanics and its Applications, Volume 360, Issue 2, 525–533.
[51] Yan, X., Wu, W., (2012). Hysteretic optimization for the capacitated vehicle routing problem, 9th IEEE International Conference on Networking, Sensing and Control (ICNSC), 12-15
[52] Pál, K. F., (2006). Hysteretic optimization for the Sherrington–Kirkpatrick spin glass, Physica A: Statistical Mechanics and its Applications, Volume 367, 261-268.
[53] Shen J H, Li Y. (2009). Light Ray Optimization and its parameter analysis, International Joint Conference on Computational Science and Optimization, 918-922.
[54] Shen, J.H. ; Li, J.L. (2012). Light Ray Optimization Algorithm Based on Annealing Strategy, Advanced Materials Research, 461, 435-439.
[55] Shen, J., Li, J., (2010). The principle analysis of Light Ray Optimization Algorithm, IEEE Second International Conference on Computational Intelligence and Natural Computing Proceedings (CINC), 154-157.
[56] Yuan, G. N., Zhang, L. N., Wang, D. D., Harbin T. L., (2013). Parameter Identification of Ship Vertical Motions using Light Ray Optimization Algorithm, Pacific PNT Conference.
[57] Kan, W., Jihong, S., (2012). Multi-Objective Light Ray Optimization, IEEE Fifth International Joint Conference on Computational Sciences and Optimization (CSO), 822-825.
[58] Shen, J., Li, J-F., Li, Y., (2012). Eulerian Light ray optimization algorithm, Journal of Harbin Engineering University, 33, 7.
[59] Kaveh, A., Ghazaan, M. I., Bakhshpoori, T., (2013). An Improved Ray Optimization Algorithm for Design of Truss Structures, Civil Engineering, 57(2), 97–112
[60] Tamura, K., Yasuda, K., (2011), Primary study of spiral dynamics inspired optimization, IEEJ Trans Electrical Electronic Eng, 6 (S1), 98–100.
[61] Tamura, K., Yasuda, K., (2011), Spiral dynamics inspired optimization, J Adv Comput Intell Intell Inform, 15 (8), 1116–1122.
[62] Benasla, L, Belmadani, A., Rahli, M., (2014). Spiral Optimization Algorithm for solving Combined Economic and Emission Dispatch, International Journal of Electrical Power & Energy Systems, Volume 62, 163–174.
[63] Phukan, R., (2013). Reactive Power Management using Firefly and Spiral Optimization under Static and Dynamic Loading Conditions, J Electr Electron Syst 2: 114.
[64] Ouadi, A., Bentarzi, H., Reciou, A., (2013). Optimal multiobjective design of digital filters using spiral optimization technique, 2, 461.
[65] Nasir, A. N. K., (2015). An Improved Spiral Dynamic Optimization Algorithm with Engineering Application, IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP, 99, 1-12.
[66] Eskandar, H., Sadollah, A., Bahreininejad, A., Hamd, M., (2012). Water cycle algorithm – a novel metaheuristic optimization method for solving constrained engineering optimization problems”, Computers & Structures, 110-111, 151-166.
[67] Sadollah, A., Eskandar, H., Bahreininejad, A., Kima, J. H., (2015). Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems, Applied Soft Computing, 30, 58-71.
[68] Haddad, O. B., Moravej, M., Loáiciga, H. A., Application of the Water Cycle Algorithm to the Optimal Operation of Reservoir System, J. Irrig. Drain Eng., 141(5), 04014064.
[69] Sadollah, A., Eskandar, H., Bahreininejad, A., Kim, J. H., (2014). Water cycle algorithm for solving multi-objective optimization problems, Soft Computing.
[70] Sadollah, A., Eskandar, H., Kim, J. H., (2015). Water cycle algorithm for solving constrained multi-objective optimization problems, Applied Soft Computing, 27, 279-298.
[71] Sadollah, A., Eskandar, H., Bahreininejad, A., Kim, J. H., (2015). Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures, Computers & Structures, 149, 1-16.
[72] Hieu, T. T., (2011). A water flow algorithm for optimization problems, PhD thesis, National University of Singapore.
[73] Brodić, D., (2011). Advantages of the Extended Water Flow Algorithm for Handwritten Text Segmentation, Lecture Notes in Computer Science Volume 6744, 418-423.
[74] Tran, T. H. and Ng, K. M. (2011). A Water Flow Algorithm for Flexible Flow Shop Scheduling with Intermediate Buffers. Journal of Scheduling, 14 (5), 483-500
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