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 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.

A GENERALIZED HYBRID GENERATION SCHEME OF DIFFERENTIAL EVOLUTION FOR GLOBAL NUMERICAL OPTIMIZATION

2011 ◽  
Vol 10 (01) ◽  
pp. 35-65 ◽  
Author(s):  
WENYIN GONG ◽  
ZHIHUA CAI ◽  
LIYUAN JIA ◽  
HUI LI
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Search Space ◽  
Mutation Operator ◽  
Test Functions ◽  
Hybrid Generation ◽  
Global Numerical Optimization ◽  
De Algorithm ◽  
Speed Up ◽  
Continuous Domain

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization over continuous domain, which has been widely used in many areas. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, two operators, the migration operator of biogeography-based optimization and the "DE/best/1" mutation operator, are employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.

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.

A new differential evolution algorithm with a combined mutation strategy for optimum synthesis of path-generating four-bar mechanisms

2016 ◽  
Vol 231 (14) ◽  
pp. 2690-2705 ◽  
Author(s):  
WY Lin ◽  
KM Hsiao
Keyword(s):  
Differential Evolution ◽  
Synthesis Method ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Dimensional Synthesis ◽  
Solution Quality ◽  
De Algorithm ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Solution Accuracy

A one-phase synthesis method using heuristic optimization algorithms can solve the dimensional synthesis problems of path-generating four-bar mechanisms. However, due to the difficulty of the problem itself, there is still room for improvement in solution accuracy and reliability. Therefore, in this study, a new differential evolution (DE) algorithm with a combined mutation strategy, termed the combined-mutation differential evolution (CMDE) algorithm, is proposed to improve the solution quality. In the combined mutation strategy, the DE/best/1 operator and the DE/current-to-best/1 operator are respectively executed on some superior parents and some mediocre parents, and the DE/rand/1 operator is executed on the other inferior parents. Furthermore, the individuals participating in the three mutation operators are randomly selected from the entire set of parents. The proposed CMDE algorithm with the three different search modes possesses better population diversity as well as search ability than the DE algorithm. The effectiveness of the proposed CMDE algorithm is demonstrated using five representative problems. Findings show a marked improvement in solution accuracy and reliability. The most accurate results are obtained with an approximate combination ratio for the three mutation operators.

scholarly journals An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 569
Author(s):  
Kai Zhang ◽  
Yicheng Yu
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Iteration Process ◽  
Excellent Performance ◽  
De Algorithm ◽  
Current Population ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Better Than

Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.

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 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.

Modified Differential Evolution Algorithm for Time-Modulated Linear Array Antenna

2020 ◽  
Vol 35 (10) ◽  
pp. 1161-1168
Author(s):  
Weilong Liang ◽  
Rui Li ◽  
Jingwei Li ◽  
Zhao Wu
Keyword(s):  
Convergence Rate ◽  
Differential Evolution ◽  
Novel Mutation ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Local Optima ◽  
Adjustment Strategy ◽  
Quadratic Interpolation ◽  
Mutation Strategy ◽  
Adaptive Adjustment

A modified differential evolution (MDE) algorithm based on a novel mutation strategy and adaptive adjustment strategy of parameter crossover rate (CR) is proposed to improve the population diversity and to avoid frapping in local optima. Also the simplified quadratic interpolation is employed to accelerate the convergence rate. Benchmark functions have been provided to verify the MDE algorithm. Compared with other improved evolutionary algorithms, experiment results reveal that the MDE has a promising performance in the convergence rate and the exploration ability. Finally, the proposed algorithm is proved to realize accelerating the optimization of time-modulated arrays (TMA).

EFFECTS OF LANGUAGE AND PRIOR KNOWLEDGE LEVELS ON HYPERTEXT READING COMPREHENSION

2020 ◽  
Author(s):  
Kshema Jose
Keyword(s):  
Reading Comprehension ◽  
Prior Knowledge ◽  
Language Proficiency ◽  
Language Competence ◽  
Additional Information ◽  
Reading Order ◽  
Low Levels ◽  
Online Texts ◽  
Characteristic Features ◽  
Better Than

<p>This study observed how two hypertext features – absence of a linear or author-specified order and availability of multiple reading aids – influence reading comprehension processes of ESL readers. Studies with native or highly proficient users of English, have suggested that readers reading hypertexts comprehend better than readers reading print texts. This was attributed to (i) presence of hyperlinks that provide access to additional information that can potentially help overcome comprehension obstacles and (ii) the absence of an author-imposed reading order that helps readers exercise cognitive flexibility. An aspect that remains largely un-researched is how well readers with low language competence comprehend hypertexts. This research sought to initiate research in the area by exploring the question: Do all ESL readers comprehend a hypertext better than a print text?</p> <p>Keeping in mind the fact that a majority of readers reading online texts in English can be hindered by three types of comprehension deficits – low levels of language proficiency, non-availability of prior knowledge, or both – this study investigated how two characteristic features of hypertext, viz., linking to additional information and non-linearity in presentation of information, affect reading comprehension of ESL readers. </p> <p>Two types of texts that occur in the electronic medium – linear or pre-structured texts and non-linear or self-navigating texts, were used in this study. Based on a comparison of subjects’ comprehension outcomes and free recalls, text factors and reader factors that can influence hypertext reading comprehension of ESL readers are identified. </p> Contradictory to what many researchers believe, results indicate that self-navigating hypertexts might not promote deep comprehension in all ESL readers.

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