Efficiency of Particle Swarm Optimization and Multiobjective Genetic Algorithm in Optimal Operation of Agricultural Water Resources

Authors

1 Department of Civil Engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran

2 Department of Civil Engineering, University of Tehran, Tehran, Iran

Abstract

The main problem of water resources planning is the inappropriate allocation between different consumers. Water allocation planning is a complex, multi-variable, and multi-constraint problem, which requires advanced optimization methods to be solved. Classical optimization methods are facing some limitations such as being trapped in local optimum points, and difficulties in handling different variables. In this paper two of these methods including particle swarm optimization, PSO and multiobjective non-dominated sorting genetic algorithms, NGGAII were explored and their efficiency in optimization water reservoir operation problems is compared. Dealing with the necessary of multiobjective programing accuracy, two single objective models was developed separately using PSO to verify the NSGAII results.

Keywords

Main Subjects

References

خاشعی سیوکی ع، قهرمان ب، کوچک­زاده م، "کاربرد تخصیص و مدیریت آب کشاورزی با استفاده از تکنیک بهینه‏ سازی PSO (مطالعه موردی: دشت نیشابور)"، نشریه آب و خاک، 1392، 27 (2)، 292-303.
مقدسی م، مرید س، عراقی‌نژاد ش، "بهینه‌سازی تخصیص آب در شرایط کم‌آبی با استفاده از روش‌های برنامه‌ریزی غیرخطی، هوش جمعی و الگوریتم ژنتیک (مطالعه موردی)"، تحقیقات منابع آب ایران، 1387، 4 (3)، 1-13.
Anwar AA, Haq ZU, “Genetic algorithms for the sequential irrigation scheduling problem”, Irrigation Science, 2013, 31 (4), 815-829.
Azamathulla HM, Wu FC, Ghani, AA, Narulkar SM, Zakaria NA, Chang CK, “Comparison between genetic algorithm and linear programming approach for real time operation”, Journal of Hydro-environment Research, 2008, 2, 172-181.
Dai C, Yao M, Xie Z, Chen C, Liu J, “Parameter optimization for growth model of greenhouse crop using genetic algorithm”, Applied Soft Computing, 2009, 9, 13-19.
Deb K, Pratap A, Agarwal S, Meyarivan T, “A fast and elitist multi-objective genetic algorithm: NSGA-II”, IEEE Transaction on Evolutionary Computation, 2002, 6, 181-197.
Goldberg DE, “Genetic algorithms in search, optimization and machine learning”, Addison-Wesley, Reading, Mass, 1989, 399.
Kursawe F, “A variant of evaluation strategies for vector optimization”, Berlin, Germany: Springer, 1990, 193-197.
Labadie JW, “Optimal operation of multi reservoir systems: state-of the-art review”, Journal of Water Resource Planning and Management, 2004, 130 (2), 93-111.
Lalehzari R, Boroomand Nasab S, Moazed H, Haghighi A, Yaghoobzadeh M, “Simulation-optimization modeling for water resources management using NSGAII-OIP and Modflow”, Irrigation and Drainage, 2020,
Lalehzari R, Boroomand Nasab S, Moazed H, Haghighi A, “Multi-objective management of water allocation to sustainable irrigation planning and optimal cropping pattern”, Journal of Irrigation and Drainage Engineering, ASCE, 2016.
Lalehzari R, Kerachian R, “Developing a framework for daily common pool groundwater allocation to demands in agricultural regions”, Agricultural Water Management. 2020b, 241, 106278.
Lalehzari R, Kerachian R, “An integrated framework for optimal irrigation planning under uncertainty: application of soil, water, atmosphere and plant modeling”, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2020a.
Schaffer JD, “Multi-objective optimization with vector evaluated genetic algorithm”, Proceedings of the First International Conference on genetic algorithm. Lawrence Erlbaum, 1987, 93-100.
Shi Y, Eberhart R, “Empirical study of particle swarm optimization”, Proceeding IEEE International Congers Evolutionary Compution, Washington, DC., USA, 1999, 1945-1950.
Yeh WWG, “Reservoir management and operation models: a state of the art review”, Water Resource Research, 1985, 21 (1), 1797-1818.
Journal of Civil and Environmental Engineering
Volume 52.4, Issue 109 - Serial Number 109
Winter 1401
February 2023
Pages 163-172

Files

Share

How to cite

Statistics