scholarly journals A Two-Stage Differential Evolution Algorithm with Mutation Strategy Combination

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2163
Author(s):  
Xingping Sun ◽  
Da Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Qingyi Chen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Two Stage ◽  
Mutation Strategy ◽  
Strategy Combination ◽  
Evolution Algorithm

For most of differential evolution (DE) algorithm variants, premature convergence is still challenging. The main reason is that the exploration and exploitation are highly coupled in the existing works. To address this problem, we present a novel DE variant that can symmetrically decouple exploration and exploitation during the optimization process in this paper. In the algorithm, the whole population is divided into two symmetrical subpopulations by ascending order of fitness during each iteration; moreover, we divide the algorithm into two symmetrical stages according to the number of evaluations (FEs). On one hand, we introduce a mutation strategy, DE/current/1, which rarely appears in the literature. It can keep sufficient population diversity and fully explore the solution space, but its convergence speed gradually slows as iteration continues. To give full play to its advantages and avoid its disadvantages, we propose a heterogeneous two-stage double-subpopulation (HTSDS) mechanism. Four mutation strategies (including DE/current/1 and its modified version) with distinct search behaviors are assigned to superior and inferior subpopulations in two stages, which helps simultaneously and independently managing exploration and exploitation in different components. On the other hand, an adaptive two-stage partition (ATSP) strategy is proposed, which can adjust the stage partition parameter according to the complexity of the problem. Hence, a two-stage differential evolution algorithm with mutation strategy combination (TS-MSCDE) is proposed. Numerical experiments were conducted using CEC2017, CEC2020 and four real-world optimization problems from CEC2011. The results show that when computing resources are sufficient, the algorithm is competitive, especially for complex multimodal problems.

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.

scholarly journals Multiscale Cooperative Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yongzhao Du ◽  
Yuling Fan ◽  
Xiaofang Liu ◽  
Yanmin Luo ◽  
Jianeng Tang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Early Stage ◽  
Differential Evolution Algorithm ◽  
Search Range ◽  
Adaptive Parameter ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
The Individual

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

A Fractal Mutation Factor Modified Differential Evolution Algorithm

2014 ◽  
Vol 598 ◽  
pp. 418-423 ◽  
Author(s):  
Xiao Hong Qiu ◽  
Bo Li ◽  
Zhi Yong Cui ◽  
Jing Li
Keyword(s):  
Brownian Motion ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Hurst Index ◽  
Test Results ◽  
Fractal Brownian Motion ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Better Than

To get better solution by improving the mutation strategy of Differential Evolution algorithm, a fractal mutation strategy is introduced. The fractal mutation factor of the proposed Fractal Mutation factor Differential Evolution (FMDE) algorithm is simulated by fractal Brownian motion with a different Hurst index. The new algorithm is test on 25 benchmark functions presented at 2005 IEEE Congress on Evolutionary Computation (CEC2005). The optimization results of at least 10 benchmark functions are significantly better than the results obtained by JADE and CoDE, and most of the rest of the test results are approximate. This shows that FMDE can significantly improve the accuracy and adaptability to solve optimization problems.

scholarly journals A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhongbo Hu ◽  
Shengwu Xiong ◽  
Zhixiang Fang ◽  
Qinghua Su
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Engineering Optimization ◽  
De Algorithm ◽  
Self Adaptation ◽  
Evolution Algorithm ◽  
Selection For ◽  
Adaptation Scheme

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.

scholarly journals pSum-SaDE: A Modifiedp-Median Problem and Self-Adaptive Differential Evolution Algorithm for Text Summarization

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Rasim M. Alguliev ◽  
Ramiz M. Aliguliyev ◽  
Chingiz A. Mehdiyev
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Median Problem ◽  
Proposed Model ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Self Adaptive

Extractive multidocument summarization is modeled as a modifiedp-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.

Logistic Map and Exponential Scaling Factor based Differential Evolution

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Differential Evolution ◽  
Logistic Map ◽  
Differential Evolution Algorithm ◽  
Scaling Factor ◽  
Exploration And Exploitation ◽  
Engineering Problems ◽  
Evolutionary Technique ◽  
Evolution Algorithm ◽  
State Of Art ◽  
Modified Algorithm

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

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