An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

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.

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

2020 ◽  
Vol 14 (1) ◽  
pp. 54-64
Author(s):  
Lam Thuan Phat ◽  
Nguyen Nhat Phi Long ◽  
Nguyen Hoai Son ◽  
Ho Huu Vinh ◽  
Le Anh Thang
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Design Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Laminated Composite ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

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.

scholarly journals An Improved Differential Evolution Algorithm Based on Dual-Strategy

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuxu Zhong ◽  
Peng Cheng
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Positive Impact ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Fitness Value ◽  
Mutation Strategy ◽  
Dual Strategy ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization problems over continuous space and has been widely used in many fields of science and engineering. How to avoid the local optimal solution and how to improve the convergence performance of DE are hotpot problems for many researchers. In this paper, an improved differential evolution algorithm based on dual-strategy (DSIDE) is proposed. The DSIDE algorithm has two strategies. (1) An enhanced mutation strategy based on “DE/rand/1,” which takes into account the influence of reference individuals on mutation and has strong global exploration and convergence ability. (2) A novel adaptive strategy for scaling factor and crossover probability based on fitness value has a positive impact on population diversity. The DSIDE algorithm is verified with other seven state-of-the-art DE variants under 30 benchmark functions. Furthermore, Wilcoxon sign rank-sum test, Friedman test, and Kruskal–Wallis test are utilized to analyze the results. The experiment results show that the proposed DSIDE algorithm can significantly improve the global optimization performance.

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