Application of particle swarm optimization to water management: an introduction and overview

• Published in: Environmental monitoring and assessment Vol. 192; no. 5; p. 281
• Main Authors: Jahandideh-Tehrani, Mahsa, Bozorg-Haddad, Omid, Loáiciga, Hugo A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2020; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Bibliometrics; Birds; Contamination; Convergence; Dynamic programming; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Evolutionary algorithms; Evolutionary computation; Fronts; Genetic algorithms; Groundwater; Groundwater quality; Groundwater runoff; hydrologic engineering; Hydrologic models; Linear programming; Literature reviews; Marine fishes; Mathematical analysis; Modelling; Monitoring/Environmental Analysis; Mutation; Optimization techniques; Parameter estimation; Particle swarm optimization; Rainfall-runoff modeling; Rainfall-runoff relationships; Reservoir operation; runoff; Simulated annealing; Support vector machines; Velocity; Water management; Water quality; Water resources; Water resources management; Water shortages; Particle swarm optimization; State-of-art review; Evolutionary algorithm; Water resources
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-020-8228-z

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall–runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.