scholarly journals Ranking-based hierarchical random mutation in differential evolution

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245887
Author(s):  
Xuxu Zhong ◽  
Meijun Duan ◽  
Peng Cheng
Keyword(s):  
Individual Differences ◽  
Differential Evolution ◽  
Control Parameter ◽  
Population Diversity ◽  
Convergence Speed ◽  
Mutation Operator ◽  
Random Mutation ◽  
Parameter Adaptation ◽  
Fitness Value ◽  
Control Parameter Adaptation

In order to improve the performance of differential evolution (DE), this paper proposes a ranking-based hierarchical random mutation in differential evolution (abbreviated as RHRMDE), in which two improvements are presented. First, RHRMDE introduces a hierarchical random mutation mechanism to apply the classic “DE/rand/1” and its variant on the non-inferior and inferior group determined by the fitness value. The non-inferior group employs the traditional mutation operator “DE/rand/1” with global and random characteristics, which increases the global exploration ability and population diversity. The inferior group uses the improved mutation operator “DE/rand/1” with elite and random characteristics, which enhances the local exploitation ability and convergence speed. Second, the control parameter adaptation of RHRMDE not only considers the complexity differences of various problems but also takes individual differences into account. The proposed RHRMDE is compared with five DE variants and five non-DE algorithms on 32 universal benchmark functions, and the results show that the RHRMDE is superior over the compared algorithms.

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 Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation

2021 ◽  
Author(s):  
Yong-Feng Ge ◽  
Jinli Cao ◽  
Hua Wang ◽  
Zhenxiang Chen ◽  
Yanchun Zhang
Keyword(s):  
Differential Evolution ◽  
Prior Knowledge ◽  
Data Storage ◽  
Population Diversity ◽  
Mutation Operator ◽  
Outsourced Data ◽  
Mutation Strategy ◽  
Discrete Domain ◽  
Continuous Domain ◽  
Better Than

AbstractBy breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k-anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population diversity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each individual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.

A K-Means Clustering Algorithm Based on Enhanced Differential Evolution

2011 ◽  
Vol 339 ◽  
pp. 71-75 ◽  
Author(s):  
Li Mao ◽  
Huai Jin Gong ◽  
Xing Yang Liu
Keyword(s):  
Differential Evolution ◽  
Numerical Experiments ◽  
Clustering Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Mutation Operator ◽  
Local Optima ◽  
Initial Cluster ◽  
Crossover Probability ◽  
Probability Operator

The conventional k-means algorithms are sensitive to the initial cluster centers, and tend to be trapped by local optima. To resolve these problems, a novel k-means clustering algorithm using enhanced differential evolution technique is proposed in this paper. This algorithm improves the global search ability by applying Laplace mutation operator and exponentially increasing crossover probability operator. Numerical experiments show that this algorithm overcomes the disadvantages of the conventional k-means algorithms, and improves search ability with higher accuracy, faster convergence speed and better robustness.

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