Application of particle swarm optimization to water management: an introduction and overview
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 alg...
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Published in | Environmental monitoring and assessment Vol. 192; no. 5; p. 281 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Springer International Publishing
01.05.2020
Springer Nature B.V |
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Abstract | 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. |
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AbstractList | 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. 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.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. |
ArticleNumber | 281 |
Author | Bozorg-Haddad, Omid Loáiciga, Hugo A. Jahandideh-Tehrani, Mahsa |
Author_xml | – sequence: 1 givenname: Mahsa surname: Jahandideh-Tehrani fullname: Jahandideh-Tehrani, Mahsa organization: School of Engineering and Built Environment, Griffith University – sequence: 2 givenname: Omid orcidid: 0000-0001-6607-9581 surname: Bozorg-Haddad fullname: Bozorg-Haddad, Omid email: OBHaddad@ut.ac.ir organization: Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran – sequence: 3 givenname: Hugo A. surname: Loáiciga fullname: Loáiciga, Hugo A. organization: Department of Geography, University of California |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32285219$$D View this record in MEDLINE/PubMed |
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SubjectTerms | 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 |
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