Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm

Eusuff, Muzaffar; Lansey, Kevin

doi:10.1061/(asce)0733-9496(2003)129:3(210)

Cited by 1,187 publications

(578 citation statements)

References 27 publications

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“…The results obtained are then shuffled so that the information can be passed. This local search and shuffling process continues until a defined convergence criterion is met (Eusuff and Lansey 2003).…”

Section: Two-stage Pso Algorithm With the Sflamentioning

confidence: 99%

See 1 more Smart Citation

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

Mao

Zhang

Duan

et al. 2017

International Journal of Green Energy

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Abstract:The power-voltage (P-V) characteristic curves of a PV array are nonlinear and have multiple peaks under partially shaded conditions (PSCs). This paper proposes a novel maximum power point tracking (MPPT) method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization (PSO) algorithm. The grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated in the basic PSO algorithm (PSO-SFLA), ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed. Tests results show that the proposed method converges in less than half the time taken by the conventional PSO method, and the power is improved by 33% under the worst PSCs, which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.

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Section: Two-stage Pso Algorithm With the Sflamentioning

confidence: 99%

“…Alternative approaches use evolutionary algorithms such as particle swarm optimization (PSO) algorithm (Eberhart and Kennedy 1995), artificial fish swarm algorithm (AFSA) (Li et al 2002), artificial bee colony (ABC) algorithm ( Karaboga 2005) or shuffled frog leaping algorithm (SFLA) (Eusuff and Lansey 2003). Among these,…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

Mao

Zhang

Duan

et al. 2017

International Journal of Green Energy

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“…In the SFLA, the population consists of a set of frogs (solutions) that is partitioned into subsets referred to as memeplexes. The different memeplexes are considered as different cultures of frogs, each performing a local search [5].…”

Section: Introductionmentioning

confidence: 99%

Improved Shuffled Frog Leaping Algorithm Based on Quantum Rotation Gates

Wu¹,

Fang²,

Li³

et al. 2017

dtcse

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Abstract. Aiming at the search speed and accuracy of the shuffled frog leaping algorithm not high, the idea of variation was integrated into the shuffled frog leaping algorithm. A new improved shuffled frog leaping algorithm was proposed which was called quantum frog leaping algorithm. The positions of frog are encoded by the probability amplitudes of quantum bits, the movements of frog are performed by quantum rotation gates, which achieve particles searching. Through the experiments on six standard functions, simulation results show the proposed algorithm has high searching efficiency and precision, Moreover, QSFLA as a promising optimization algorithm has strong convergence and high stability.

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“…In the field of hydraulic engineering, some evolutionary optimization techniques, such as memetic algorithms (Baños et al 2007), SFLA (Eusuff and Lansey 2003) or harmony search (Geem 2006), have been successfully applied to the design of water distribution networks. The flexibility of these methods has facilitated the development of hybrid models that combine features of different methodologies.…”

Section: Introductionmentioning

confidence: 99%

Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

Mora-Meliá

Iglesias-Rey

Martínez-Solano

et al. 2013

Water Resour Manage

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Genetic algorithms (GA) are optimization techniques that are widely used in the design of water distribution networks. One of the main disadvantages of GA is positional bias, which degrades the quality of the solution. In this study, a modified pseudo-genetic algorithm (PGA) is presented. In a PGA, the coding of chromosomes is performed using integer coding; in a traditional GA, binary coding is utilized. Each decision variable is represented by only one gene. This variation entails a series of special characteristics in the definition of mutation and crossover operations.Some benchmark networks have been used to test the suitability of a PGA for designing water distribution networks. More than 50,000 simulations were conducted with different sets of parameters. A statistical analysis of the obtained solutions was also performed. Through this analysis, more suitable values of mutation and crossover probabilities were discovered for eachcase. The results demonstrate the validity of the method.Optimum solutions are not guaranteed in any heuristic method. Hence, the concept of a "good solution" is introduced. A good solution is a design solution that does not substantially exceed the optimal solution that is obtained from the simulations. This concept may be useful when the computational cost is critical. The main conclusion derived from this study is that a proper combination of population and crossover and mutation probabilities leads to a high probability that good solutions will be obtained.

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Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm

Cited by 1,187 publications

References 27 publications

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

Improved Shuffled Frog Leaping Algorithm Based on Quantum Rotation Gates

Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

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