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Ilonen, Jarmo; Kämäräinen, Joni-Kristian; Lampinen, Jouko

doi:10.1023/a:1022995128597

Cited by 511 publications

(59 citation statements)

References 18 publications

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“…It has been used successfully in many fields [12 -15]. This algorithm is derived from the genetic algorithm but easier to get applied without encoding and decoding operation [13]. Moreover, the correlation of multivariate is considered to a certain extent, differential evolution algorithm has a great advantage in the variable coupling problem than the particle swarm optimization [16].…”

Section: Related Studies On Optimization Algorithmsmentioning

confidence: 99%

Tunnel Engineering Seepage Parameters Intelligent Back Analysis Based on FEM and Differential Evolution Algorithm

Ma¹,

Jiang²,

Wang³

2016

TOCIEJ

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Abstract:To determine the seepage parameters of surrounding rock during excavation of tunnels, a program of groundwater seepage (SEEP) was developed. Then finite element models are established in SEEP with the consideration of seepage equations for continuous medium, and the correctness was verified by the comparison of FLAC 3D . Moreover, seepage back analysis program (SBAP) was developed in C++ language by combining differential evolution algorithm (DE) and SEEP, and the measured values of water pressure and flow were used as input data, SBAP was applied to predict the horizontal and vertical hydraulic conductivities of the different stratum. The hydraulic conductivities predicted were inputted to SEEP, the computed values of water pressure and flow calculated by SEEP are in good agreement with the measured values. SBAP shows the faster convergence speed and precise feedback results. This program provides a means for engineers and researchers to determine seepage parameters in the seepage analysis of similar tunnel projects.

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Section: Related Studies On Optimization Algorithmsmentioning

confidence: 99%

Tunnel Engineering Seepage Parameters Intelligent Back Analysis Based on FEM and Differential Evolution Algorithm

Ma¹,

Jiang²,

Wang³

2016

TOCIEJ

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“…Even if the number of evaluations used in the present approach includes gradient and Hessian computations only the Terminal repeller unconstrained subenergy transformation algorithm used by Jiang et al 32 needs fewer evaluations. Figure 4 compares the convergences of the GTS and of the Differential evolution [42][43][44][45][46] algorithm for the two dimensional Rastrigin function. The Differential evolution algorithm runs were performed with the Mathematica.…”

Section: Tests Of the Efficiencymentioning

confidence: 99%

“…34 To get more information, we also performed test runs with some algorithms implemented into the Mathematica 5.1 program package. These tests were performed with the Random search [47][48][49][50] and the Differential evolution [42][43][44][45][46] approaches. While the former were only used for the two dimensional case, the latter were employed for all dimensionalities.…”

Section: Tests Of the Efficiencymentioning

confidence: 99%

Gradient tabu search

Stepanenko

Engels

2006

J Comput Chem

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This paper presents a modification of the tabu search called gradient tabu search (GTS). It uses analytical gradients for a fast minimization to the next local minimum and analytical diagonal elements of the Hessian to escape local minima. For an efficient blocking of already visited areas tabu regions and tabu directions are introduced into the tabu list (TL). Trials with various well-known test functions indicate that the GTS is a very promising approach to determine local and global minima of differentiable functions. Possible application areas could be optimization routines for force field parameters or conformational searches for large molecules.

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“…In this scheme, they used a pool of three trial vector generation strategies and a pool of three parameter setting combinations. The trial vector strategies used are rand/1, rand/2, and current to rand/1, while the parameter setting combi- The popularity of DE due to its advantages over other evolutionary methods can be observed in its diverse applications in real life fields [22][23][24][25][26][27][28][29] . But the intensive and random development of DE algorithm has created several inconsistencies in the naming and formulation of trial vector generation schemes.…”

Section: Introductionmentioning

confidence: 99%

The analysis, identification and measures to remove inconsistencies from differential evolution mutation variants

Abbas¹,

Ahmad²,

Jabeen³

2017

ScienceAsia

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Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in recent years. A number of mutation variants have been established for this algorithm. These mutation variants make the DE algorithm more applicable, but random development of these variants has created inconsistencies such as naming and formulation. Hence this study aims to identify inconsistencies and to propose solutions to make them consistent. Most of the inconsistencies exist because of the uncommon nomenclature used for these variants. In this study, a comprehensive study is carried out to identify inconsistencies in the nomenclature of mutation variants that do not match each other. Appropriate and consistent names are proposed for them. The proposed names assigned to conflicting variants are based on the name of the variant, the total number of vectors used to generate the trial vector, and the order of the vectors to form the equation of these mutation variants. To ensure the performance diversity of the consistent set of DE mutation strategies, experimental results are generated using a test suit of benchmark functions.

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Untitled

Cited by 511 publications

References 18 publications

Tunnel Engineering Seepage Parameters Intelligent Back Analysis Based on FEM and Differential Evolution Algorithm

Tunnel Engineering Seepage Parameters Intelligent Back Analysis Based on FEM and Differential Evolution Algorithm

Gradient tabu search

The analysis, identification and measures to remove inconsistencies from differential evolution mutation variants

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