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

Abstract

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 2 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.

Optimization Model for Agricultural Reclaimed Water Allocation Using Mixed-Integer Nonlinear Programming

“…Multi-objective programming has been developed to analyze water allocation where more than one objective must be considered. A fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) was presented by Rezaei, et al [23] to improve conjunctive surface water and groundwater management in Najafabad Plain, Iran. The model used a weighting method to define the partial performance of each objective's potential solution to reach an optimal solution on the Pareto front.…”

Multigene Genetic-Programming-Based Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets

“…Over recent years, artificial intelligence (AI) techniques have been widely introduced to solve water and marine engineering problems. For example, Kisi et al [20] presented the earliest application of the adaptive neuro fuzzy inference system (ANFIS) to estimate sediment in rivers; Hipni et al [21] employed the support vector machine (SVM) and ANFIS to forecast the daily dam water levels, Rezaei et al [22,23] proposed the fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) algorithm for conjunctive water use management, and Bashiri et al [24] used the harmony search algorithm and artificial neural networks (ANN) to predict local scour depth downstream of sluice gates. Recently, Moroni et al [25] reviewed the literature regarding an environmental decision support system for oil spill management, and stated that the provision of support services is generally based on AI paradigms.…”

Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

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