f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management

• We propose a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO).
• The f-MOPSO algorithm was compared with two other popular MOPSOs through solving a bi-objective conjunctive water use.
• The f-MOPSO outperformed the other MOPSO algorithms with reference to different performance criteria.
• The method is able of finding the unique optimal solution to facilitate water planners to take the best operating policy.

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.