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Bio-inspired optimization of sustainable energy systems: a review. (English) Zbl 1296.90154
Summary: Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

90C90 Applications of mathematical programming
91B76 Environmental economics (natural resource models, harvesting, pollution, etc.)
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[1] E. Vine, “Breaking down the silos: the integration of energy efficiency, renewable energy, demand response and climate change,” Energy Efficiency, vol. 1, no. 1, pp. 49-63, 2008. · doi:10.1007/s12053-008-9004-z
[2] H. Lund, “Renewable energy strategies for sustainable development,” Energy, vol. 32, no. 6, pp. 912-919, 2007. · doi:10.1016/j.energy.2006.10.017
[3] A. Hepbasli, “A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future,” Renewable and Sustainable Energy Reviews, vol. 12, no. 3, pp. 593-661, 2008. · doi:10.1016/j.rser.2006.10.001
[4] R. Chedid and Y. Saliba, “Optimization and control of autonomous renewable energy systems,” International Journal of Energy Research, vol. 20, no. 7, pp. 609-624, 1996.
[5] S. Iniyan and K. Sumathy, “The application of a Delphi technique in the linear programming optimization of future renewable energy options for India,” Biomass and Bioenergy, vol. 24, no. 1, pp. 39-50, 2003. · doi:10.1016/S0961-9534(02)00089-2
[6] G. Privitera, A. R. Day, G. Dhesi, and D. Long, “Optimising the installation costs of renewable energy technologies in buildings: a linear programming approach,” Energy and Buildings, vol. 43, no. 4, pp. 838-843, 2011. · doi:10.1016/j.enbuild.2010.12.003
[7] M. Gong, “Optimization of industrial energy systems by incorporating feedback loops into the MIND method,” Energy, vol. 28, no. 15, pp. 1655-1669, 2003. · doi:10.1016/S0360-5442(03)00170-1
[8] P. Liu, D. I. Gerogiorgis, and E. N. Pistikopoulos, “Modeling and optimization of polygeneration energy systems,” Catalysis Today, vol. 127, no. 1-4, pp. 347-359, 2007. · doi:10.1016/j.cattod.2007.05.024
[9] T. Ikegami, Y. Iwafune, and K. Ogimoto, “Development of the optimum operation scheduling model of domestic electric appliances for the supply-demand adjustment in a power system,” IEEJ Transactions on Power and Energy, vol. 130, no. 10, pp. 877-887, 2010. · doi:10.1541/ieejpes.130.877
[10] B. Wille-Haussmann, T. Erge, and C. Wittwer, “Decentralised optimisation of cogeneration in virtual power plants,” Solar Energy, vol. 84, no. 4, pp. 604-611, 2010. · doi:10.1016/j.solener.2009.10.009
[11] H. Morais, P. Kádár, P. Faria, Z. A. Vale, and H. M. Khodr, “Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming,” Renewable Energy, vol. 35, no. 1, pp. 151-156, 2010. · doi:10.1016/j.renene.2009.02.031
[12] S. Ruangpattana, D. Klabjan, J. Arinez, and S. Biller, “Optimization of on-site renewable energy generation for industrial sites,” in Proceedings of IEEE/PES Power Systems Conference and Exposition (PSCE ’11), March 2011. · doi:10.1109/PSCE.2011.5772448
[13] C. A. Babu and S. Ashok, “Optimal utilization of renewable energy-based IPPs for industrial load management,” Renewable Energy, vol. 34, no. 11, pp. 2455-2460, 2009. · doi:10.1016/j.renene.2009.02.032
[14] Z. Kravanja, “Mathematical programming approach to sustainable system synthesis,” Chemical Engineering Transactions, vol. 21, pp. 481-486, 2010.
[15] A. Borghetti, M. Bosetti, S. Grillo et al., “Short-term scheduling and control of active distribution systems with high penetration of renewable resources,” IEEE Systems Journal, vol. 4, no. 3, pp. 313-322, 2010. · doi:10.1109/JSYST.2010.2059171
[16] A. Vergnano, C. Thorstensson, B. Lennartson, et al., “Modeling and optimization of energy consumption in cooperative multi-robot systems,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 2, pp. 423-428, 2012. · doi:10.1109/TASE.2011.2182509
[17] E. D. Castronuovo and J. A. P. Lopes, “Optimal operation and hydro storage sizing of a wind-hydro power plant,” International Journal of Electrical Power and Energy System, vol. 26, no. 10, pp. 771-778, 2004. · doi:10.1016/j.ijepes.2004.08.002
[18] N. Löhndorf and S. Minner, “Optimal day-ahead trading and storage of renewable energies-an approximate dynamic programming approach,” Energy Systems, vol. 1, no. 1, pp. 61-77, 2010. · doi:10.1007/s12667-009-0007-4
[19] V. Marano, G. Rizzo, and F. A. Tiano, “Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage,” Applied Energy, vol. 97, pp. 849-859, 2012. · doi:10.1016/j.apenergy.2011.12.086
[20] A. Sinha and P. Chaporkar, “Optimal power allocation for a renewable energy source,” in Proceedings of National Conference on Communications, 2012.
[21] Y. P. Cai, G. H. Huang, Z. F. Yang, Q. G. Lin, and Q. Tan, “Community-scale renewable energy systems planning under uncertainty-an interval chance-constrained programming approach,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 721-735, 2009. · doi:10.1016/j.rser.2008.01.008
[22] S. Tomasin and T. Erseghe, “Constrained optimization of local sources generation in smart grids by SDP approximation,” in Proceedings of IEEE International Symposium on Power Line Communications and Its Applications (ISPLC ’11), pp. 187-192, April 2011. · doi:10.1109/ISPLC.2011.5764388
[23] M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, A Series of Books in the Mathematical Sciences, W. H. Freeman, San Francisco, Calif, USA, 1979. · Zbl 0411.68039
[24] R. Baños, F. Manzano-Agugliaro, F. G. Montoya, C. Gil, A. Alcayde, and J. Gómez, “Optimization methods applied to renewable and sustainable energy: a review,” Renewable and Sustainable Energy Reviews, vol. 15, no. 4, pp. 1753-1766, 2011. · doi:10.1016/j.rser.2010.12.008
[25] K. Chau, “A review on the integration of artificial intelligence into coastal modeling,” Journal of Environmental Management, vol. 80, no. 1, pp. 47-57, 2006. · doi:10.1016/j.jenvman.2005.08.012
[26] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Morgan Kaufmann, Waltham, Mass, USA, 3rd edition, 2012. · Zbl 1230.68018
[27] M. Li, S. C. Lim, and W. Zhao, “Cauchy-Matern model of sea surface wind speed at the Lake Worth, Florida,” Mathematical Problems in Engineering, vol. 2012, Article ID 843676, 10 pages, 2012. · Zbl 1264.86015 · doi:10.1155/2012/843676
[28] M. Li, Y. Q. Chen, J.-Y. Li, and W. Zhao, “Hölder scales of sea level,” Mathematical Problems in Engineering, vol. 2012, Article ID 863707, 22 pages, 2012. · Zbl 06173650 · doi:10.1155/2012/863707
[29] J. Makhoul, “Linear prediction: a tutorial review,” Proceedings of the IEEE, vol. 63, no. 4, pp. 561-580, 1975.
[30] B. S. Atal, “The history of linear prediction,” IEEE Signal Processing Magazine, vol. 23, no. 2, pp. 154-161, 2006.
[31] M. Li, W. Zhao, and B. Chen, “Heavy-tailed prediction error: a difficulty in predicting biomedical signals of 1/f noise type,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 291510, 5 pages, 2012. · Zbl 1261.92029 · doi:10.1155/2012/291510
[32] M. Kawashima, “Artificial neural network backpropagation model with three-phase annealing developed for the building energy predictor shootout,” ASHRAE Transactions, vol. 100, no. 2, pp. 1096-1103, 1994.
[33] S. M. Islam, S. M. Al-Alawi, and K. A. Ellithy, “Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network,” Electric Power Systems Research, vol. 34, no. 1, pp. 1-9, 1995.
[34] A. Al-Shehri, “Artificial neural network for forecasting residential electrical energy,” International Journal of Energy Research, vol. 23, no. 8, pp. 649-659, 1999.
[35] A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, “A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran,” Energy Policy, vol. 36, no. 7, pp. 2637-2644, 2008. · doi:10.1016/j.enpol.2008.02.035
[36] G. J. Tsekouras, N. D. Hatziargyriou, and E. N. Dialynas, “An optimized adaptive neural network for annual midterm energy forecasting,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 385-391, 2006. · doi:10.1109/TPWRS.2005.860926
[37] A. Sözen, M. A. Ak\ccayol, and E. Arcaklio\uglu, “Forecasting net energy consumption using artificial neural network,” Energy Sources, Part B, vol. 1, no. 2, pp. 147-155, 2006.
[38] A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, “Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors,” Energy Conversion and Management, vol. 49, no. 8, pp. 2272-2278, 2008. · doi:10.1016/j.enconman.2008.01.035
[39] A. H. Neto and F. A. S. Fiorelli, “Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption,” Energy and Buildings, vol. 40, no. 12, pp. 2169-2176, 2008. · doi:10.1016/j.enbuild.2008.06.013
[40] R. Yokoyama, T. Wakui, and R. Satake, “Prediction of energy demands using neural network with model identification by global optimization,” Energy Conversion and Management, vol. 50, no. 2, pp. 319-327, 2009. · doi:10.1016/j.enconman.2008.09.017
[41] Z. W. Geem and W. E. Roper, “Energy demand estimation of South Korea using artificial neural network,” Energy Policy, vol. 37, no. 10, pp. 4049-4054, 2009. · doi:10.1016/j.enpol.2009.04.049
[42] K. Ermis, A. Midilli, I. Dincer, and M. A. Rosen, “Artificial neural network analysis of world green energy use,” Energy Policy, vol. 35, no. 3, pp. 1731-1743, 2007. · doi:10.1016/j.enpol.2006.04.015
[43] J. L. Bosch, G. López, and F. J. Batlles, “Daily solar irradiation estimation over a mountainous area using artificial neural networks,” Renewable Energy, vol. 33, no. 7, pp. 1622-1628, 2008. · doi:10.1016/j.renene.2007.09.012
[44] J. Cao and X. Lin, “Application of the diagonal recurrent wavelet neural network to solar irradiation forecast assisted with fuzzy technique,” Engineering Applications of Artificial Intelligence, vol. 21, no. 8, pp. 1255-1263, 2008. · doi:10.1016/j.engappai.2008.02.003
[45] P. L. Zervas, H. Sarimveis, J. A. Palyvos, and N. C. G. Markatos, “Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques,” Renewable Energy, vol. 33, no. 8, pp. 1796-1803, 2008. · doi:10.1016/j.renene.2007.09.020
[46] W. Huang, C. Murray, N. Kraus, and J. Rosati, “Development of a regional neural network for coastal water level predictions,” Ocean Engineering, vol. 30, no. 17, pp. 2275-2295, 2003. · doi:10.1016/S0029-8018(03)00083-0
[47] M. H. Kazeminezhad, A. Etemad-Shahidi, and S. J. Mousavi, “Application of fuzzy inference system in the prediction of wave parameters,” Ocean Engineering, vol. 32, no. 14-15, pp. 1709-1725, 2005. · doi:10.1016/j.oceaneng.2005.02.001
[48] A. Mellit, S. A. Kalogirou, L. Hontoria, and S. Shaari, “Artificial intelligence techniques for sizing photovoltaic systems: a review,” Renewable and Sustainable Energy Reviews, vol. 13, no. 2, pp. 406-419, 2009. · doi:10.1016/j.rser.2008.01.006
[49] A. Mellit, M. Benghanem, A. H. Arab, and A. Guessoum, “Modelling of sizing the photovoltaic system parameters using artificial neural network,” in Proceedings of IEEE Conference on Control Applications, vol. 1, pp. 353-357, June 2003.
[50] A. Mellit, M. Benghanem, A. H. Arab, and A. Guessoum, “An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria,” Renewable Energy, vol. 30, no. 10, pp. 1501-1524, 2005. · doi:10.1016/j.renene.2004.11.012
[51] L. Hontoria, J. Aguilera, and P. Zufiria, “A new approach for sizing stand alone photovoltaic systems based in neural networks,” Solar Energy, vol. 78, no. 2, pp. 313-319, 2005. · doi:10.1016/j.solener.2004.08.018
[52] A. Mellit, M. Benghanem, A. Hadj Arab, and A. Guessoum, “Identification and modeling of the optimal sizing combination of stand-alone photovoltaic systems using the radial basis function networks,” in Proceedings of the World Renewable Energy Congress VIII (WREC ’04), Denver, Colo, USA, 2004.
[53] J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Mich, USA, 1975. · Zbl 0429.03045
[54] D. B. Fogel, “Introduction to simulated evolutionary optimization,” IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 3-14, 1994. · doi:10.1109/72.265956
[55] I. Rechenberg, Evolutions Strategies: Optimierung Technischer Systemenach Prinzipien der Biologischen Evolution, Frommberg-Holzboog, Stuttgart, Germany, 1973.
[56] R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341-359, 1997. · Zbl 0888.90135 · doi:10.1023/A:1008202821328
[57] D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702-713, 2008. · Zbl 05740389 · doi:10.1109/TEVC.2008.919004
[58] K. Miettinen, Nonlinear Multiobjective Optimization, International Series in Operations Research & Management Science, 12, Kluwer Academic Publishers, Boston, Mass, USA, 1999. · Zbl 0949.90082
[59] Q. S. Li, D. K. Liu, J. Q. Fang, and C. M. Tam, “Multi-level optimal design of buildings with active control under winds using genetic algorithms,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 86, no. 1, pp. 65-86, 2000. · doi:10.1016/S0167-6105(00)00004-0
[60] H. Li, Z. Chen, and H. Polinder, “Optimization of multibrid permanent-magnet wind generator systems,” IEEE Transactions on Energy Conversion, vol. 24, no. 1, pp. 82-92, 2009. · doi:10.1109/TEC.2008.2005279
[61] S. A. Grady, M. Y. Hussaini, and M. M. Abdullah, “Placement of wind turbines using genetic algorithms,” Renewable Energy, vol. 30, no. 2, pp. 259-270, 2005. · doi:10.1016/j.renene.2004.05.007
[62] A. Emami and P. Noghreh, “New approach on optimization in placement of wind turbines within wind farm by genetic algorithms,” Renewable Energy, vol. 35, no. 7, pp. 1559-1564, 2010. · doi:10.1016/j.renene.2009.11.026
[63] Varun and Siddhartha, “Thermal performance optimization of a flat plate solar air heater using genetic algorithm,” Applied Energy, vol. 87, no. 5, pp. 1793-1799, 2010. · doi:10.1016/j.apenergy.2009.10.015
[64] M. Zagrouba, A. Sellami, M. Bouaïcha, and M. Ksouri, “Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction,” Solar Energy, vol. 84, no. 5, pp. 860-866, 2010. · doi:10.1016/j.solener.2010.02.012
[65] K. Tselepidou and K. L. Katsifarakis, “Optimization of the exploitation system of a low enthalpy geothermal aquifer with zones of different transmissivities and temperatures,” Renewable Energy, vol. 35, no. 7, pp. 1408-1413, 2010. · doi:10.1016/j.renene.2009.11.004
[66] S. H. El-Hefnawi, “Photovoltaic diesel-generator hybrid power system sizing,” Renewable Energy, vol. 13, no. 1, pp. 33-40, 1998.
[67] R. Dufo-López and J. L. Bernal-Agustín, “Design and control strategies of PV-diesel systems using genetic algorithms,” Solar Energy, vol. 79, no. 1, pp. 33-46, 2005. · doi:10.1016/j.solener.2004.10.004
[68] E. Koutroulis, D. Kolokotsa, A. Potirakis, and K. Kalaitzakis, “Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms,” Solar Energy, vol. 80, no. 9, pp. 1072-1088, 2006. · doi:10.1016/j.solener.2005.11.002
[69] R. Dufo-López, J. L. Bernal-Agustín, and J. Contreras, “Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage,” Renewable Energy, vol. 32, no. 7, pp. 1102-1126, 2007. · doi:10.1016/j.renene.2006.04.013
[70] T. Senjyu, D. Hayashi, A. Yona, N. Urasaki, and T. Funabashi, “Optimal configuration of power generating systems in isolated island with renewable energy,” Renewable Energy, vol. 32, no. 11, pp. 1917-1933, 2007. · doi:10.1016/j.renene.2006.09.003
[71] D. Fogel and K. Chellapilla, “Revisiting evolutionary programming,” in Applications and Science of Computational Intelligence, Proceedings of SPIE, pp. 2-11, Orlando, Fla, USA, 1998. · doi:10.1117/12.304792
[72] T. D. H. Cau and R. J. Kaye, “Multiple distributed energy storage scheduling using constructive evolutionary programming,” in Proceedings of the 22nd IEEE International Conference on Power Industry Computer Applications, pp. 402-407, May 2001.
[73] K. F. Fong, V. I. Hanby, and T. T. Chow, “HVAC system optimization for energy management by evolutionary programming,” Energy and Buildings, vol. 38, no. 3, pp. 220-231, 2006. · doi:10.1016/j.enbuild.2005.05.008
[74] I. F. MacGill, “An evolutionary programming tool for assessing the operational value of distributed energy resources within restructured electricity industries,” in Proceedings of the Australasian Universities Power Engineering (AUPEC ’07), pp. 1-6, Australasian Universities, December 2007. · doi:10.1109/AUPEC.2007.4548130
[75] S. Chen, Y. Zheng, C. Cattani, and W. Wang, “Modeling of biological intelligence for SCM system optimization,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 769702, 10 pages, 2012. · Zbl 1235.90022 · doi:10.1155/2012/769702
[76] Y. C. Chang, “Optimal chiller loading by evolution strategy for saving energy,” Energy and Buildings, vol. 39, no. 4, pp. 437-444, 2007. · doi:10.1016/j.enbuild.2005.12.009
[77] T. Logenthiran, D. Srinivasan, A. M. Khambadkone, and T. Sundar Raj, “Optimal sizing of distributed energy resources for integrated microgrids using evolutionary strategy,” in Proceedings of IEEE Congress on Evolutionary Computation, pp. 1-8, 2012.
[78] M. A. Falcone, H. S. Lopes, and L. dos Santos Coelho, “Supply chain optimisation using evolutionary algorithms,” International Journal of Computer Applications in Technology, vol. 31, no. 3-4, pp. 158-167, 2008. · doi:10.1504/IJCAT.2008.018154
[79] S. Chakraborty, T. Senjyu, A. Yona, A. Y. Saber, and T. Funabashi, “Fuzzy unit commitment strategy integrated with solar energy system using a modified differential evolution approach,” in Proceedings of Asia and Pacific Conference & Exposition on Transmission & Distribution, pp. 1-4, October 2009. · doi:10.1109/TD-ASIA.2009.5357006
[80] L. Slimani and T. Bouktir, “Application of differential evolution algorithm to optimal power flow with high wind energy penetration,” Acta Electrotehnica, vol. 53, no. 1, pp. 59-68, 2012.
[81] L. dos Santos Coelho, A. D. V. De Almeida, and V. C. Mariani, “Cultural differential evolution approach to optimize the economic dispatch of electrical energy using thermal generators,” in Proceedings of the 13th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ’08), pp. 1378-1383, September 2008. · doi:10.1109/ETFA.2008.4638578
[82] R. Suzuki, F. Kawai, S. Kitagawa et al., “The \epsilon constrained differential evolution approach for optimal operational planning of energy plants,” in Proceedings of IEEE Congress on Evolutionary Computation (CEC ’10), July 2010. · doi:10.1109/CEC.2010.5586322
[83] H. A. Hejazi, H. R. Mohabati, S. H. Hosseinian, and M. Abedi, “Differential evolution algorithm for security-constrained energy and reserve optimization considering credible contingencies,” IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1145-1155, 2011. · doi:10.1109/TPWRS.2010.2084112
[84] W. S. Lee, Y. T. Chen, and Y. Kao, “Optimal chiller loading by differential evolution algorithm for reducing energy consumption,” Energy and Buildings, vol. 43, no. 2-3, pp. 599-604, 2011. · doi:10.1016/j.enbuild.2010.10.028
[85] L. Peng, Y. Wang, G. Dai, Y. Chang, and F. Chen, “Optimization of the Earth-Moon low energy transfer with differential evolution based on uniform design,” in Proceedings of IEEE Congress on Evolutionary Computation (CEC ’10), July 2010. · doi:10.1109/CEC.2010.5586384
[86] N. Srinivas and K. Deb, “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary Computing, vol. 2, pp. 221-248, 1994.
[87] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002. · Zbl 05451853 · doi:10.1109/4235.996017
[88] E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257-271, 1999.
[89] E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: improving the strength Pareto evolutionary algorithm,” in Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, K. Giannakoglou, D. Tsahalis, J. Periaux, P. Papailou, and T. Fogarty, Eds., Athens, Greece, 2001.
[90] J. D. Knowles and D. W. Corne, “M-PAES: a memetic algorithm for multiobjective optimization,” in Proceedings of the Congress on Evolutionary Computation (CEC ’00), vol. 1, pp. 325-332, July 2000.
[91] H. A. Abbass, R. Sarker, and C. Newton, “PDE: a pareto-frontier differential evolution approach for multi-objective optimization problems,” in Proceedings of IEEE Congress on Evolutionary Computation, vol. 2, pp. 971-978, May 2001.
[92] R. Angira and B. V. Babu, “Non-dominated sorting differential evolution (NSDE): an extension of differential evolution for multi-objective optimization,” in Proceedings of 2nd Indian International Conference on Artificial Intelligence, 2005.
[93] Y. J. Zheng, Q. Song, and S. Y. Chen, “Multiobjective fireworks optimization for variable-rate fertilization in oil crop production,” Applied Soft Computing. In press.
[94] E. Benini and A. Toffolo, “Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation,” Journal of Solar Energy Engineering, vol. 124, no. 4, pp. 357-363, 2002. · doi:10.1115/1.1510868
[95] M. Zhao, Z. Chen, and F. Blaabjerg, “Optimisation of electrical system for offshore wind farms via genetic algorithm,” IET Renewable Power Generation, vol. 3, no. 2, pp. 205-216, 2009. · doi:10.1049/iet-rpg:20070112
[96] A. Kusiak, Z. Zhang, and M. Li, “Optimization of wind turbine performance with data-driven models,” IEEE Transactions on Sustainable Energy, vol. 1, no. 2, pp. 66-76, 2010. · doi:10.1109/TSTE.2010.2046919
[97] A. Kusiak and Z. Song, “Design of wind farm layout for maximum wind energy capture,” Renewable Energy, vol. 35, no. 3, pp. 685-694, 2010. · doi:10.1016/j.renene.2009.08.019
[98] J. L. Bernal-Agustín, R. Dufo-López, and D. M. Rivas-Ascaso, “Design of isolated hybrid systems minimizing costs and pollutant emissions,” Renewable Energy, vol. 31, no. 14, pp. 2227-2244, 2006. · doi:10.1016/j.renene.2005.11.002
[99] R. Dufo-López and J. L. Bernal-Agustín, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems,” Renewable Energy, vol. 33, no. 12, pp. 2559-2572, 2008. · doi:10.1016/j.renene.2008.02.027
[100] B. Ould Bilal, V. Sambou, P. A. Ndiaye, C. M. F. Kébé, and M. Ndongo, “Optimal design of a hybrid solar-wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP),” Renewable Energy, vol. 35, no. 10, pp. 2388-2390, 2010. · doi:10.1016/j.renene.2010.03.004
[101] F. G. Montoya, R. Banos, C. Gil, A. Espin, A. Alcayde, and J. Gomez, “Minimization of voltage deviation and power losses in power networks using Pareto optimization methods,” Engineering Applications of Artificial Intelligence, vol. 23, pp. 695-703, 2010.
[102] Y. Thiaux, J. Seigneurbieux, B. Multon, and H. Ben Ahmed, “Load profile impact on the gross energy requirement of stand-alone photovoltaic systems,” Renewable Energy, vol. 35, no. 3, pp. 602-613, 2010. · doi:10.1016/j.renene.2009.08.005
[103] P. Rao and C. H. Peng, “A research on power dispatch of energy-saving and emission-reduction generation based on the improved differential evolution algorithm,” Journal of East China Jiaotong University, vol. 5, pp. 48-52, 2010.
[104] P. Tarasewich and P. R. McMullen, “Swarm intelligence powers in numbers,” Communications of the ACM, vol. 45, no. 8, pp. 62-67, 2002. · Zbl 05394323 · doi:10.1145/545151.545152
[105] E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, New York, NY, USA, 1999. · Zbl 1003.68123
[106] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, Perth, Australia, December 1995.
[107] A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed optimization by ant colonies,” in Proceedings of European Conference on Artificial Life, pp. 134-142, Paris, France, 1991.
[108] X. S. Yang, “Engineering optimizations via nature-inspired virtual bee algorithms,” in Proceedings of the 1st International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC ’05), vol. 3562 of Lecture Notes in Computer Science, pp. 317-323, Springer, Las Palmas, Spain, June 2005.
[109] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007. · Zbl 1149.90186 · doi:10.1007/s10898-007-9149-x
[110] J. D. Farmer, N. H. Packard, and A. S. Perelson, “The immune system, adaptation, and machine learning,” Physica D, vol. 22, no. 1-3, pp. 187-204, 1986. · doi:10.1016/0167-2789(86)90240-X
[111] J. Kennedy, “Bare bones particle swarms,” in Proceedings of IEEE Swarm Intelligence Symposium, pp. 120-127, IEEE Press, 2003.
[112] M. R. AlRashidi and K. M. EL-Naggar, “Long term electric load forecasting based on particle swarm optimization,” Applied Energy, vol. 87, no. 1, pp. 320-326, 2010. · doi:10.1016/j.apenergy.2009.04.024
[113] T. Niknam and B. B. Firouzi, “A practical algorithm for distribution state estimation including renewable energy sources,” Renewable Energy, vol. 34, no. 11, pp. 2309-2316, 2009. · doi:10.1016/j.renene.2009.03.005
[114] N. Amjady and H. R. Soleymanpour, “Daily Hydrothermal Generation Scheduling by a new Modified Adaptive Particle Swarm Optimization technique,” Electric Power Systems Research, vol. 80, no. 6, pp. 723-732, 2010. · doi:10.1016/j.epsr.2009.11.004
[115] Z. H. Zhan, J. Zhang, Y. Li, and H. S. H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 6, pp. 1362-1381, 2009. · doi:10.1109/TSMCB.2009.2015956
[116] Y.-J. Zheng, H.-F. Ling, and Q. Guan, “Adaptive parameters for a modified comprehensive learning particle swarm optimizer,” Mathematical Problems in Engineering, vol. 2013, 11 pages, 2013. · Zbl 06173093 · doi:10.1155/2012/207318
[117] T. Y. Lee, “Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach,” Energy Conversion and Management, vol. 49, no. 4, pp. 751-760, 2008. · doi:10.1016/j.enconman.2007.07.019
[118] C. Kongnam and S. Nuchprayoon, “A particle swarm optimization for wind energy control problem,” Renewable Energy, vol. 35, no. 11, pp. 2431-2438, 2010. · doi:10.1016/j.renene.2010.02.020
[119] S. Khanmohammadi, M. Amiri, and M. T. Haque, “A new three-stage method for solving unit commitment problem,” Energy, vol. 35, no. 7, pp. 3072-3080, 2010. · doi:10.1016/j.energy.2010.03.049
[120] P. R. López, F. Jurado, N. Ruiz-Reyes, S. García Galán, and M. Gómez, “Particle swarm optimization for biomass-fuelled systems with technical constraints,” Engineering Applications of Artificial Intelligence, vol. 21, no. 8, pp. 1389-1396, 2008. · doi:10.1016/j.engappai.2008.04.013
[121] P. R. Lopez, S. G. Galan, N. Ruiz-Reyes, and F. Jurado, “A method for particle swarm optimization and its application in location of biomass power plants,” International Journal of Green Energy, vol. 5, no. 3, pp. 199-211, 2008. · doi:10.1080/15435070802107165
[122] T. Y. Lee and C. L. Chen, “Wind-photovoltaic capacity coordination for a time-of-use rate industrial user,” IET Renewable Power Generation, vol. 3, no. 2, pp. 152-167, 2009. · doi:10.1049/iet-rpg:20070068
[123] A. K. Kaviani, G. H. Riahy, and S. M. Kouhsari, “Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages,” Renewable Energy, vol. 34, no. 11, pp. 2380-2390, 2009. · doi:10.1016/j.renene.2009.03.020
[124] A. Kornelakis and E. Koutroulis, “Methodology for the design optimisation and the economic analysis of grid-connected photovoltaic systems,” IET Renewable Power Generation, vol. 3, no. 4, pp. 476-492, 2009. · doi:10.1049/iet-rpg.2008.0069
[125] A. Kornelakis and Y. Marinakis, “Contribution for optimal sizing of grid-connected PV-systems using PSO,” Renewable Energy, vol. 35, no. 6, pp. 1333-1341, 2010. · doi:10.1016/j.renene.2009.10.014
[126] S. M. Hakimi and S. M. Moghaddas-Tafreshi, “Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran,” Renewable Energy, vol. 34, no. 7, pp. 1855-1862, 2009. · doi:10.1016/j.renene.2008.11.022
[127] A. Mahor, V. Prasad, and S. Rangnekar, “Economic dispatch using particle swarm optimization: a review,” Renewable and Sustainable Energy Reviews, vol. 13, no. 8, pp. 2134-2141, 2009. · doi:10.1016/j.rser.2009.03.007
[128] Z. L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Transactions on Power Systems, vol. 18, no. 3, pp. 1187-1195, 2003. · doi:10.1109/TPWRS.2003.814889
[129] D. N. Jeyakumar, T. Jayabarathi, and T. Raghunathan, “Particle swarm optimization for various types of economic dispatch problems,” International Journal of Electrical Power & Energy Systems, vol. 28, no. 1, pp. 36-42, 2006. · doi:10.1016/j.ijepes.2005.09.004
[130] L. Wang and C. Singh, “Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search,” Engineering Applications of Artificial Intelligence, vol. 22, no. 2, pp. 298-307, 2009. · doi:10.1016/j.engappai.2008.07.007
[131] K. K. Mandal, M. Basu, and N. Chakraborty, “Particle swarm optimization technique based short-term hydrothermal scheduling,” Applied Soft Computing Journal, vol. 8, no. 4, pp. 1392-1399, 2008. · Zbl 05738681 · doi:10.1016/j.asoc.2007.10.006
[132] R. Chakrabarti, P. K. Chattopadhyay, M. Basu, and C. K. Panigrahi, “Particle swarm optimization technique for dynamic economic dispatch,” Journal of the Institution of Engineers, vol. 87, pp. 48-54, 2006.
[133] J. B. Park, Y. W. Jeong, H. H. Kim, and J. R. Shin, “An improved particle swarm optimization for economic load dispatch with valve point effect,” International Journal of Innovations in Energy Systems and Power, vol. 1, no. 1, 2006.
[134] L. dos Santos Coelho and V. C. Mariani, “Economic dispatch optimization using hybrid chaotic particle swarm optimizer,” in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC ’07), pp. 1963-1968, October 2007. · doi:10.1109/ICSMC.2007.4414152
[135] J. Cai, X. Ma, L. Li, and P. Haipeng, “Chaotic particle swarm optimization for economic dispatch considering the generator constraints,” Energy Conversion and Management, vol. 48, no. 2, pp. 645-653, 2007. · doi:10.1016/j.enconman.2006.05.020
[136] A. I. S. Kumar, K. Dhanushkodi, J. J. Kumar, and C. K. C. Paul, “Particle swarm optimization solution to emission and economic dispatch problem,” in Proceedings of IEEE Confernce on Covergent Technologies for the Asia-Pacific Region (TENCON ’03), vol. 1, pp. 435-439, October 2003.
[137] B. Zhao, C. Guo, and Y. Cao, “Dynamic economic dispatch in electricity market using particle swarm optimization algorithm,” in Proceedings of 5th World Congress on Intelligent Control and Automation (WCICA ’04), pp. 5050-5054, June 2004.
[138] M. A. Abido, “Multiobjective particle swarm for environmental/economic dispatch problem,” in Proceedings of the 8th International Power Engineering Conference (IPEC ’07), pp. 1385-1390, December 2007.
[139] M. A. Alrashidi and M. E. Hawary, “Impact of loading conditions on the emission economic dispatch,” in Proceedings of World Academy of Science and Engineering and Technology, pp. 148-151, 2008.
[140] Z. Li, X. P. Chang, and J. H. Qin, “Application of ant colony algorithms to optimization design of solar energy dynamic power system in space station,” Proceedings of the Chinese Society of Electrical Engineering, vol. 25, pp. 294-298, 2005.
[141] D. Xu, L. Kang, and B. Cao, “Graph-based ant system for optimal sizing of standalone hybrid wind/PV power systems,” in Computational Intelligence, vol. 4114 of Lecture Notes in Computer Science, pp. 1136-1146, Springer, 2006.
[142] W. K. Foong, H. R. Maier, and A. R. Simpson, “Power plant maintenance scheduling using ant colony optimization: an improved formulation,” Engineering Optimization, vol. 40, no. 4, pp. 309-329, 2008. · doi:10.1080/03052150701775953
[143] L. Warner and U. Vogel, “Optimization of energy supply networks using ant colony optimization,” in Proceedings of 22th International Conference on Informatics for Environmental Protection, pp. 327-334, 2008.
[144] P. C. See, V. C. Tai, and M. Molinas, “Ant colony optimization applied to control of ocean wave energy converters,” Energy Procedia, vol. 20, pp. 148-155, 2012.
[145] M. D. Toksari, “Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey,” Energy Policy, vol. 37, no. 3, pp. 1181-1187, 2009. · doi:10.1016/j.enpol.2008.11.017
[146] O. Baskan, S. Haldenbilen, H. Ceylan, and H. Ceylan, “Estimating transport energy demand using ant colony optimization,” Energy Sources, Part B, vol. 7, no. 2, pp. 188-199, 2012. · Zbl 1162.90590 · doi:10.1080/15567240903030513
[147] A. Afshar, O. Bozorg Haddad, M. A. Mariño, and B. J. Adams, “Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation,” Journal of the Franklin Institute, vol. 344, no. 5, pp. 452-462, 2007. · Zbl 1269.90142 · doi:10.1016/j.jfranklin.2006.06.001
[148] T. Niknam, S. I. Taheri, J. Aghaei, S. Tabatabaei, and M. Nayeripour, “A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources,” Applied Energy, vol. 88, no. 12, pp. 4817-4830, 2011.
[149] T. Niknam, H. D. Mojarrad, H. Z. Meymand, and B. B. Firouzi, “A new honey bee mating optimization algorithm for non-smooth economic dispatch,” Energy, vol. 36, no. 2, pp. 896-908, 2011. · doi:10.1016/j.energy.2010.12.021
[150] F. S. Abu-Mouti and M. E. El-Hawary, “Optimal dynamic economic dispatch including renewable energy source using artificial bee colony algorithm,” in Proceedings of IEEE Systems Conference, 2012.
[151] D. Vera, J. Carabias, F. Jurado, and N. Ruiz-Reyes, “A Honey Bee Foraging approach for optimal location of a biomass power plant,” Applied Energy, vol. 87, no. 7, pp. 2119-2127, 2010. · doi:10.1016/j.apenergy.2010.01.015
[152] W. C. Hong, “Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm,” Energy, vol. 36, no. 9, pp. 5568-5578, 2011. · doi:10.1016/j.energy.2011.07.015
[153] T. K. Abdul Rahman, Z. M. Yasin, and W. N. W. Abdullah, “Artificial-immune-based for solving economic dispatch in power system,” in Proceedings of National Power and Energy Conference (PECon ’04), pp. 31-35, November 2004.
[154] L. D. S. Coelho and V. C. Mariani, “Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units,” Chaos, Solitons & Fractals, vol. 40, no. 5, pp. 2376-2383, 2009. · doi:10.1016/j.chaos.2007.10.032
[155] P. Arsalani and M. Seddighizadeh, “Minimizing the loss of energy in transmission systems with capacitor placement using an immune algorithm and fuzzy logic,” in Proceedings of 2nd Conference on Energy Management and Conservation, 2012.
[156] A. Mellit and S. A. Kalogirou, “Application of neural networks and genetic algorithms for predicting the optimal sizing coefficient of photovoltaic supply (PVS) systems,” in Proceedings of the World Renewable Energy Congress IX and Exhibition, 2006.
[157] A. Mellit, “ANFIS-based genetic algorithm for predicting the optimal sizing coefficient of photovoltaic supply (PVS) systems,” in Proceedings of 3rd International Conference on Thermal Engineering: Theory and Applications, pp. 96-102, 2007.
[158] Y. P. Chang and C. N. Ko, “A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters,” Expert Systems with Applications, vol. 36, no. 3, pp. 6809-6816, 2009. · doi:10.1016/j.eswa.2008.08.007
[159] A. Li, L. Wang, J. Li, and C. Ji, “Application of immune algorithm-based particle swarm optimization for optimized load distribution among cascade hydropower stations,” Computers and Mathematics with Applications, vol. 57, no. 11-12, pp. 1785-1791, 2009. · Zbl 1186.90134 · doi:10.1016/j.camwa.2008.10.016
[160] Z. X. Yang, X. F. Yue, and L. Wang, “Study on the energy consumption optimization in a central air-conditioning system based on bee evolutionary genetic algorithm method,” Building Science, vol. 27, no. 6, pp. 78-82, 2011.
[161] M. S. Kıran, E. Özceylan, M. Gündüz, and T. Paksoy, “A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey,” Energy Conversion and Management, vol. 53, no. 1, pp. 75-83, 2012.
[162] A. Ghanbari, M. Kazemi, F. Mehmanpazir, and M. M. Nakhostin, “A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems,” Knowledge-Based Systems, vol. 39, pp. 194-206, 2013. · doi:10.1016/j.knosys.2012.10.017
[163] B. Tudu, S. Majumder, K. K. Mandal, and N. Chakraborty, “Comparative performance study of genetic algorithm and particle swarm optimization applied on off-grid renewable hybrid energy system,” in Swarm, Evolutionary, and Memetic Computing, vol. 7076 of Lecture Notes in Computer Science, pp. 151-158, Springer, 2011. · Zbl 06065827 · doi:10.1007/978-3-642-27172-4_19
[164] M. Carlini and S. Castellucci, “Modelling and simulation for energy production parametric dependence in greenhouses,” Mathematical Problems in Engineering, vol. 2010, Article ID 590943, 28 pages, 2010. · Zbl 1205.93109 · doi:10.1155/2010/590943
[165] M. Carlini, S. Castellucci, M. Guerrieri, and T. Honorati, “Stability and control for energy production parametric dependence,” Mathematical Problems in Engineering, vol. 2010, Article ID 842380, 21 pages, 2010. · Zbl 1205.93109 · doi:10.1155/2010/842380 · eudml:225888
[166] M. Fadaee and A. M. Radzi, “Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: a review,” Renewable and Sustainable Energy Reviews, vol. 16, no. 5, pp. 3364-3369, 2012.
[167] J. Gruska, Quantum Computing, Advanced Topics in Computer Science Series, McGraw-Hill, New York, NY, USA, 1999.
[168] G. P\uaun, G. Rozenberg, and A. Salomaa, DNA Computing: New Computing Paradigms, Texts in Theoretical Computer Science. An EATCS Series, Springer, Berlin, Germany, 1998. · Zbl 0940.68053
[169] M. Joyeux, S. Buyukdagli, and M. Sanrey, “1/f fluctuations of DNA temperature at thermal denaturation,” Physical Review E, vol. 75, no. 6, Article ID 061914, 9 pages, 2007. · doi:10.1103/PhysRevE.75.061914
[170] A. Castro, M. A. L. Marques, D. Varsano, F. Sottile, and A. Rubio, “The challenge of predicting optical properties of biomolecules: what can we learn from time-dependent density-functional theory?” Comptes Rendus Physique, vol. 10, no. 6, pp. 469-490, 2009. · doi:10.1016/j.crhy.2008.09.001
[171] C. Cattani, E. Laserra, and I. Bochicchio, “Simplicial approach to fractal structures,” Mathematical Problems in Engineering, vol. 2012, Article ID 958101, 21 pages, 2012. · Zbl 1264.28005 · doi:10.1155/2012/958101
[172] C. Cattani, “On the existence of wavelet symmetries in archaea DNA,” Computational and Mathematical Methods in Medicine, Article ID 673934, 21 pages, 2012. · Zbl 1234.92014 · doi:10.1155/2012/673934
[173] G. Zhang, “Quantum-inspired evolutionary algorithms: a survey and empirical study,” Journal of Heuristics, vol. 17, no. 3, pp. 303-351, 2011. · Zbl 1214.68378 · doi:10.1007/s10732-010-9136-0
[174] E. G. Bakhoum and C. Toma, “Dynamical aspects of macroscopic and quantum transitions due to coherence function and time series events,” Mathematical Problems in Engineering, vol. 2010, Article ID 428903, 13 pages, 2010. · Zbl 1191.35219 · doi:10.1155/2010/428903 · eudml:225118
[175] E. G. Bakhoum and C. Toma, “Mathematical transform of traveling-wave equations and phase aspects of quantum interaction,” Mathematical Problems in Engineering, vol. 2010, Article ID 695208, 15 pages, 2010. · Zbl 1191.35220 · doi:10.1155/2010/695208 · eudml:229128
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