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

Author(s):  
Lam Thuan Phat ◽  
Nguyen Nhat Phi Long ◽  
Nguyen Hoai Son ◽  
Ho Huu Vinh ◽  
Le Anh Thang

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.

2014 ◽  
Vol 596 ◽  
pp. 216-221
Author(s):  
Zong Fu Wu

An improved differential evolution algorithm for solving nonlinear equations of electrical motor system is explored. The algorithm is to convert equations into an optimization problem and, by keeping consideration of the evolution process and adopting dynamic parameters adjusting mechanism, the algorithm can improve searching efficiency and implement real-time surveillance for population overlapping. The Chaos searching strategy is used for overlapping individual to further improve the ability of global optimization. Analysis results of induction motor motion parameters show that the improved differential evolution algorithm proposed in this paper has high efficiency and powerful global optimization searching ability.


2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.


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