Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

Wang, Yong; Cai, Zixing; Zhang, Qingfu

doi:10.1109/tevc.2010.2087271

Cited by 1,301 publications

(604 citation statements)

References 22 publications

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“…Stability is computed with Spencer's method of slices implemented in MATLAB (MathWorks 2015) by Tabarroki (2011) and uses an efficient genetic algorithm (Wang 2011) to search for circular and non-circular surfaces that minimize safety factor, FS. The stability code is modified to allow specification of pore pressures from the finite element seepage solution and computed safety factors are generally found to be within 0.01 of FS computed with commercial slope stability software.…”

Section: Mechanistic Levee Modelmentioning

confidence: 99%

Stochastic Analysis of Levee Stability Subject to Variable Seepage Conditions

2017

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Levee stability is highly influenced by seepage. Specifically, hydraulic conductivity distribution within a levee influences pore pressure distribution and controls the drained strength of the soil. In this study the influence of hydraulic conductivity and blanket layer thickness on failure probability is evaluated within the context of reliability analyses that also include soil strength and unit weight as random variables. First-Order Reliability Method (FORM) is used to evaluate reliability, rank random variables by importance and to obtain sensitivity of the solution to each random variable and its distribution parameters. Stability is computed using Spencer's method of slices coupled to a finite element seepage code to directly evaluate pore pressure. In addition, response surface method solutions are compared to the direct reliability solution to assess response surface accuracy. The results show that blanket layer thickness is more important than most strength parameters and that the uncertainty in hydraulic conductivity is less important to the variance in safety factor for the case of a low-permeability blanket layer. Numerical challenges caused by implementing a finite element limit-state function are discussed. Response surface methods are found to give a reasonable approximation to the direct reliability solution when the design point is between response surface fitting points.

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Section: Mechanistic Levee Modelmentioning

confidence: 99%

Stochastic Analysis of Levee Stability Subject to Variable Seepage Conditions

2017

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“…CoDE [35] combines three well-studied trial vector generation strategies with three random control parameter settings to generate trial vectors. In L-SHADE [36], the Linear Population Size Reduction (LPSR) is embedded into SHADE so that the robustness of the algorithm is improved.…”

Section: Generation Strategy Of Differential Evolutionmentioning

confidence: 99%

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

2016

Algorithms

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Abstract:The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.

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“…Although it has been reported that differential evolution performs better than many other algorithms, it is still a dream for differential evolution users to have a strategy perfectly balancing exploration and exploitation, or equivalently, reliability and efficiency . It has been well known that the critical idea behind the success of Differential Evolution is the creative invention of differential mutation [11]. Different differential mutation strategies balance exploration and exploitation differently.…”

Section: Initialize Particles With Random Positionandvelocityvectorsmentioning

confidence: 99%

Synthesis of Non-Uniform Amplitude equally Spaced Antenna Arrays Using PSO and DE Algorithms

Yasoda¹,

Kumari²

2014

IOSRJECE

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Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

Cited by 1,301 publications

References 22 publications

Stochastic Analysis of Levee Stability Subject to Variable Seepage Conditions

Stochastic Analysis of Levee Stability Subject to Variable Seepage Conditions

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

Synthesis of Non-Uniform Amplitude equally Spaced Antenna Arrays Using PSO and DE Algorithms

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