Static and frequency optimization of folded laminated composite plates using an adjusted Differential Evolution algorithm and a smoothed triangular plate element

Le-Anh, L.; Nguyen-Thoi, T.; Ho-Huu, V.; Dang-Trung, H.; Bui-Xuan, T.

doi:10.1016/j.compstruct.2015.02.069

Cited by 67 publications

(14 citation statements)

References 56 publications

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“…The population is initialized by randomizing individuals within a search space. The population then undergoes mutation, and an individual is generated by using the following equation [ 38 – 43 ]: where x is an individual from the population r ϵ (1, NP ), G is the generation or iteration, NP is the population size, and F is the differential weight. However, not all particles from the mutation will be used in the next operation, depending on crossover probability.…”

Section: Principles Of De Algorithmmentioning

confidence: 99%

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

Kok

Rajendran

2016

PLoS ONE

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The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.

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Section: Principles Of De Algorithmmentioning

confidence: 99%

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

Kok

Rajendran

2016

PLoS ONE

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“…Loja et al used differential evolution to obtain a deflection profile minimization of magneto-electro-elastic composite structures [19]. Le-Anh et al used the finite element method and a variation of differential evolution to study folded laminated composite plates [20] and Roque and Martins used differential evolution to improve the meshless radial basis function method in the study of composite plates in bending [21].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution for optimization of functionally graded beams

Roque

Martins

2015

Composite Structures

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“…Wu and Tseng [16] solve the COP of the truss structures using a multi-population Differential Evolution with a penalty-based, self-adaptive strategy. Le-Anh et al [17] used an adjusted Differential Evolution algorithm combining with smoothed triangular plate elements for static analysis and frequency optimization of folded laminated composite plates. Ho-Huu et al [18] proposed a new version of the Differential Evolution algorithm to optimize the shape and size of truss with discrete variables.…”

Section: Introductionmentioning

confidence: 99%

Global optimization of laminated composite beams using an improved differential evolution algorithm

Phat

Long

Sơn

et al. 2020

STCE

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Differential Evolution (DE) is an efficient and effective algorithm recently proposed for solving optimization problems. In this paper, an improved version of Differential Evolution algorithm, called iDE, is introduced to solve design optimization problems of composite laminated beams. The beams used in this research are Timoshenko beam models computed based on analytical formula. The iDE is formed by modifying the mutation and the selection step of the original algorithm. Particularly, individuals involved in mutation were chosen by Roulette wheel selection via acceptant stochastic instead of the random selection. Meanwhile, in selection phase, the elitist operator is used for the selection progress instead of basic selection in the optimization process of the original DE algorithm. The proposed method is then applied to solve two problems of lightweight design optimization of the Timoshenko laminated composite beam with discrete variables. Numerical results obtained have been compared with those of the references and proved the effectiveness and efficiency of the proposed method. Keywords: improved Differential Evolution algorithm; Timoshenko composite laminated beam; elitist operator; Roulette wheel selection; deterministic global optimization.

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Static and frequency optimization of folded laminated composite plates using an adjusted Differential Evolution algorithm and a smoothed triangular plate element

Cited by 67 publications

References 56 publications

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

Differential evolution for optimization of functionally graded beams

Global optimization of laminated composite beams using an improved differential evolution algorithm

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