Rezaei, F., Safavi, H. R., Mirchi, A., Madani, K., (2017), f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management, Journal of Hydro-environment Research 14, 1–18
f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management
Farshad Rezaei, PhD Candidate, Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran
Hamid R. Safavi, Associate Prof., Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran, firstname.lastname@example.org
Ali Mirchi, Department of Civil Engineering and Center for Environmental Resource Management, University of Texas at El Paso, El Paso, USA
Kaveh Madani,Centre for Environmental Policy, Imperial College London, London, UK
In recent years, evolutionary techniques have been widely used to search for the global optimum of combinatorial non-linear non-convex problems. In this paper, we present a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) to improve conjunctive surface water and groundwater management. The f-MOPSO algorithm is simple in concept, easy to implement, and computationally efficient. It is based on the role of weighting method to define partial performance of each point (solution) in the objective space. The proposed algorithm employs a fuzzy inference system to consider all the partial performances for each point when optimizing the objective function values. The f-MOPSO algorithm was compared with two other well-known MOPSOs through a case study of conjunctive use of surface and groundwater in Najafabad Plain in Iran considering two management models, including a typical 12-month operation period and a 10-year planning horizon. Overall, the f-MOPSO outperformed the other MOPSO algorithms with reference to performance criteria and Pareto-front analysis while nearly fully satisfying water demands with least monthly and cumulative groundwater level (GWL) variation. The proposed algorithm is capable of finding the unique optimal solution on the Pareto-front to facilitate decisions to address large-scale optimization problems.