Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production

Garousi‐Nejad, Irene; Bozorg‐Haddad, Omid; Loáiciga, Hugo A.; Mariňo, Miguel A.

doi:10.1061/(asce)ir.1943-4774.0001064

Cited by 73 publications

(19 citation statements)

References 48 publications

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“…Each OEA run produces a different solution that is near the global optimum (these are called near-optimal solutions). Therefore, it is customary to present results in terms of the average of the solutions calculated from the multiple runs of the OEA (10 runs are typical, see Garousi-Nejad et al, 2016). The OEA in SOLVER is a modified version of the classic GA (Holland, 1975).…”

Section: Resultsmentioning

confidence: 99%

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

Sadeghi¹,

Loáiciga

Kharaghani³

2017

J American Water Resour Assoc

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This study develops and tests a novel optimization method for optimally selecting and sizing stormwater control measures (SCMs) in urban landscapes for selected design storms. The developed methodology yields SCMs that capture and retain stormwater via onsite percolation, remove stormwater pollutants, and minimize stormwater control expenditures. The resulting environmental optimization problem involves integer and real variables imbedded in an objective function that is subjected to multiple constraints. This study's methodology aims at practicality and ease of implementation in the solution of the SCM sizing and selection optimization problem while taking into account the main factors that govern stormwater management in urban landscapes. The near-optimal global solution of the SCM selection and design problem is obtained with nonlinear programming and verified with the average of multiple solutions calculated with multiple runs of an optimization evolutionary algorithm. The developed methodology is illustrated with one stormwater project in the City of Los Angeles, California.

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Section: Resultsmentioning

confidence: 99%

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

Sadeghi¹,

Loáiciga

Kharaghani³

2017

J American Water Resour Assoc

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“…The results indicated that the amount of variation in objective function is insignificant for the BA, and the coefficient of variation of the BA is almost 16 times less than that of the GA over 10 runs. In another study, the performance of firefly algorithm (FA) was compared with the GA for optimizing irrigation supply and hydropower generation (Garousi-Nejad et al 2016). According to the comparison, the average values of objective function were closer to the NLP compared with that of the GA, which indicated the superiority of the FA over the GA. Ehteram et al (2017a) also compared the GA's performance with the shark algorithm.…”

Section: The Binary-coded Gamentioning

confidence: 99%

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2019

Environ Monit Assess

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Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

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“…The biography-based optimization algorithm (BBA) has been used for the optimization of a four-reservoir system, and the aim of the problem was increasing the generated benefit based on generated hydropower [17]. Moreover, the firefly algorithm (FA) has been applied to multireservoir systems with the aim of increasing hydropower production [18]. In addition, the monarch butterfly (MBF) algorithm and krill algorithm (KA) have been applied for optimized hydropower generation based on the optimal operation rule for either single or multiple dam and reservoir systems [7,19].…”

Section: Review and Motivationmentioning

confidence: 99%

“…RMSE (root mean square error). The root mean square error between the generated hydropower and hydropower point capacity, as shown by Equation (18). A low value for this index represents better performance of the algorithm based on more generated hydropower:…”

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confidence: 99%

New Evolutionary Algorithm for Optimizing Hydropower Generation Considering Multireservoir Systems

et al. 2019

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In recent decades, solving complex real-life optimization problems has attracted the full attention of researchers. Dam and reservoir operation rules are considered one of the most complicated optimization engineering problems. In fact, the operation rules of dams and reservoirs are multisystematic and highly stochastic and have highly nonlinear system constraints due to the direct influence of environmental conditions: Therefore, these rules are considered highly complex optimization problems. Recently, metaheuristic methods inferred from nature have been broadly utilized to elucidate the way optimal solutions are provided for several complex optimization engineering applications, and these methods have achieved interesting results. The major advantage of these metaheuristic methods over conventional methods is the unnecessity to identify a particular initial condition, convexity, continuity, or differentiability. The present study investigated the potential of using a new metaheuristic method (i.e., the crow algorithm (CA)) to provide optimal operations for multireservoir systems, with the aim of optimally improving hydropower generation. A multireservoir system in China was considered to examine the performance of the proposed optimization algorithm for several operation scenarios. The results obtained the average hydropower generation by considering all examined operation scenarios based on the operation rule achieved using the CA, which outperformed the other metaheuristic methods. In addition, compared to other metaheuristic methods, the proposed CA lessened the time required to search for the optimal solution. In conclusion, the proposed CA has high potential for achieving optimal solutions to complex optimization problems associated with dam and reservoir operations.

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Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production

Cited by 73 publications

References 48 publications

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

New Evolutionary Algorithm for Optimizing Hydropower Generation Considering Multireservoir Systems

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