مدیریت بهینه برداشت منابع آب‌های زیرزمینی با استفاده از الگوریتم‌های NSGA-Ⅱ، SPEA-Ⅱ و PESA-Ⅱ (مطالعه موردی: دشت سیلاخور)

1.Abedinpour, A., Jabbarzadeh, A., and Yahyaei, M. 2018. A multi objective mathematical modeling for crop planning problem under Z-number uncertainty. Journal of Water and Soil Conservation. 25: 5. 1-24. (In Persian)

2.Aghajani, A., Bidabadi, F.S., Joolaei, R., and Keramatzadeh, A. 2013. Managing cropping patterns agricultural crops of Three Counties of Mazandarn province of Iran. International Journal of Agriculture and Crop Sciences. 5: 6. 596-602.
(In Persian)

3.Coello, C.A.C. 2002. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering. 191: 1245-1287.‏

4.Coello, C.A.C., Lamont, G.B., andVan Veldhuizen, D.A. 2007. Evolutionary algorithms for solving multi-objective problems. NewYork, Springer, pp. 91-94.

5.Corne, D.W., Jerram, N.R., Knowles, J.D., and Oates, M.J. 2001. July.PESA-II: Region-based selection in evolutionary multiobjective optimization. In Proceedings of the 3^rd Annual Conference on Genetic and Evolutionary Computation. pp. 283-290.

6.Corne, D.W., Knowles, J.D., andOates, M.J. 2000. September. ThePareto envelope-based selection algorithm for multiobjective optimization. In International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg. pp.  839-848.

7.Darwish, M.R., Sharara, M., Sidahmed, M., and Haidar, M. 2007. The impact of a storage facility on optimality conditions of wastewater reuse in land application: A case study in Lebanon. Resources, conservation and recycling. 51: 1. 175-189. (In Persian)

8.Davijani, M.H., Banihabib, M.E., Anvar, A.N., and Hashemi, S.R. 2016.Multi-objective optimization modelfor the allocation of water resourcesin arid regions based on the maximization of socioeconomic efficiency. Water resources management. 30: 3. 927-946.(In Persian)

9.Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. 2000. September. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In International conference on parallel problem solving from nature. Springer, Berlin, Heidelberg. pp. 849-858.

10.Ghadimi, S., and Ketabchi, H. 2019. Impact assessment of different management strategies implementation on the aquifer using numerical simulation (Case study: Namdan aquifer, Fars province, Iran). Journal of Water and Soil Conservation. 25: 6. 1-23.(In Persian)

11.Meftah Halaghi, M., Ghorbani, K., Keramatzadeh, A., and Salarijazi, M. 2020. Crop pattern optimization by using Goal programming (case study: Gharesoo basin). Journal of Waterand Soil Conservation. 27: 1. 163-180. (In Persian)

12.Homaifar, A., Qi, C.X., and Lai,S.H. 1994. Constrained optimizationvia genetic algorithms. Simulation.62: 4. 242-253.

13.Jian-Xia, C., Qiang, H., and Yi-Min, W. 2005. Genetic algorithms for optimal reservoir dispatching. Water Resources Management. 19: 4. 321-331.

14.Joines, J.A., and Houck, C.R. 1994, June. On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. In Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence. IEEE. pp. 579-584.

15.Kashefi Nezhad, P., Hooshmand, A., and Boroomandnasab, S. 2019. Optimal allocation of water resources using non-dominated sorting genetic algorithm (case study: Hamidiya irrigation network). Journal of Water and Soil Conservation. 25: 6. 239-253. (In Persian)

16.Kim, T.W., and Valdés, J.B. 2003. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering. 8: 6. 319-328.

17.Komasi, M., and Goudarzi, H.2021. Multi-objective optimization of groundwater monitoring network using a probability Pareto genetic algorithm and entropy method (case study: Silakhor plain). Journal of Hydroinformatics.23 :1. 136-150.

18.Komasi, M., and Sharghi, S. 2016. Data mining methods in time series analysis. Naghous. 156p. (In Persian)

19.Koza, J.R., and Poli, R. 1992. Genetic programming: on the programming of computers by means of natural selection. MIT press. Chapter 5.

20.McCulloch, W.S., and Pitts, W. 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics. 5: 4. 115-133.

21.Michalewicz, Z. 1995. Genetic algorithms, numerical optimization, and constraints. In Proceedings of the sixth international conference on genetic algorithms. Morgan Kauffman San Mateo. 195: 151-158.

22.Mohammadian, F., Shahnooshi, N., Gorbani, M., and Aghel, H. 2002. Development of a sustainable crop pattern in Fariman-Torbat Jam plain. Journal of Agricultural Economics.4: 2. 1-41. (In Persian)

23.Parsopoulos, K.E., and Vrahatis, M.N. 2002. Particle swarm optimization method for constrained optimization problems. Intelligent Technologies–Theory and Application: New Trends in Intelligent Technologies. 76: 1. 214-220.

24.Peralta, R.C., Forghani, A., and Fayad, H. 2014. Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow. Journal of Hydrology. 511: 776-785.‏

25.Ristikj, J., Tripcheva-Trajkovska, L., Rikaloski, I., and Markovska, L.1999. Optimization of refinery product blending by using linear programming. In Production and application of oil products. Symposium proceedings.

26.Sahoo, B., Lohani, A.K., and Sahu, R. K. 2006. Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning. Water resources management. 20: 6. 931-948.

27.Sarker, R., and Ray T. 2009. An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and Electronics in Agriculture. 68: 2. 191-199.

28.Singh, D.K., Jaiswal, C.S., Reddy, K.S., Singh, R.M., and Bhandarkar, D.M. 2001. Optimal cropping pattern in a canal command area. Agricultural Water Management. 50: 1. 1-8.

29.Thu Bui, L., and Alam, S. 2008.Multi-Objective Optimization in Computational Intelligence: Theory and Practice: Theory and Practice. IGI global. pp. 151-188.

30.Wang, Z., and Zhou, Z. 2004. Optimization of water allocation in canal systems of Chengai irrigation area. Nature and Science. 2: 1. 89-94.

31.Yang, J.M., Chen, Y.P., Horng, J.T., and Kao, C.Y. 1997. April. Applying family competition to evolution strategies
for constrained optimization. In International conference on evolutionary programming. Springer, Berlin, Heidelberg. pp. 201-211

32.Yeniay, Ö. 2005. Penalty function methods for constrained optimization with genetic algorithms. Mathematical and computational Applications.10: 1. 45-56.

33.Zitzler, E. 1999. Evolutionary algorithms for multiobjective optimization: Methods and applications. Ithaca: Shaker, 32p.‏

34.Zitzler, E., Laumanns, M., and Thiele, L. 2001. SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103p.