Differential evolution algorithm with multi-population cooperation and multi-strategy integration

Li, Xiaoyü; Wang, Lei; Jiang, Qiaoyong; Li, Ning

doi:10.1016/j.neucom.2020.09.007

Cited by 53 publications

(16 citation statements)

References 48 publications

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“…New mutation strategies are proposed in recent years, such as random neighbor based mutation ('DE/neighbor/1') [46], new triangular mutation [28], mutation vector inspired from the biological phenomenon called Hemostasis [41], an enhanced mutation strategy with time stamp scheme [47] and 'DE/pbad-to-pbest-to-gbest/1' [48].…”

Section: Mutation Operationmentioning

confidence: 99%

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

et al. 2021

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Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

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Section: Mutation Operationmentioning

confidence: 99%

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

et al. 2021

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“…Eqs (3) ~( 4) are adopted to obtain the controllable correlation and regularity of data. On the premise of the identification of the good failure prediction method, a dynamic prediction and evaluation integration strategy of controllable relevance export goods sales information is proposed [17]. Eq (8) displays its function, which is to calculate the membership M (S i , S j ) of CVV (S i , S j ) for dynamic optimization integration of Qn (tj i , tj j ):…”

Section: Plos Onementioning

confidence: 99%

International trade and finance exploration using network model of computer trade platform

Zhang

Yuan

2021

PLoS ONE

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International trade becomes increasingly frequent with the deepening of economic globalization. In order to ensure the stable and rapid development of international trade and finance, it is particularly crucial to predict the sales trend of foreign trade goods in advance through the network model of computer trade platform. To optimize the accuracy of sales forecasts for foreign trade goods, under the background of "Internet plus foreign trade", the controllable relevance big data mining of foreign trade goods sales, personalized prediction mechanism, intelligent prediction algorithm, improved distributed quantitative and centralized qualitative calculation are taken as the premise to design dynamic prediction model on export sales based on controllable relevance big data of cross border e-commerce (DPMES). Moreover, after the related experiments and comparative discussions, the forecast error ratios from the first quarter to the fourth quarter are 2.3%, 2.1%, 2.4% and 2.4% respectively, which are also within the acceptable range. The experimental results show that the design combines the advantages of openness and extensibility of Internet plus with dynamic prediction of big data, and achieves the wisdom, quantitative and qualitative prediction of the volume of goods sold under the background of "Internet plus foreign trade", which is controlled by the relevant data of foreign trade. The overall performance of this design is stronger than the previous models, has better dynamic evolution and high practical significance, and is of great significance in the development of international trade and finance.

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“…2) Multi-population inspired multi-scale information aggregation. Some works [26,70] introduce multipopulation evolutionary algorithm to solve the optimization problems, which adopts different searching regions to more efficiently enhance the diversity of individuals and can obtain a better model performance significantly. As shown in Figure 1-(c), N Long-Distance Population could supplement more diverse and richer cues, while N Short-Distance Population focuses on providing general evolutionary features.…”

Section: Introductionmentioning

confidence: 99%

“…In detail, this variant is a particular global-local search hybrid: the global character is given by the traditional EA, while the local aspect is mainly performed through constructive methods and intelligent local search heuris-tics [50]. Analogously, some later works [26,70] introduce a multi-population evolutionary algorithm to solve the constrained function optimization problems relatively efficiently, which adopts different searching regions to enhance the diversity of individuals that improves the model ability dramatically. This strategy inspires us to design a basic feature extraction module for vision transformer: whether a similar multi-scale manner can be adopted to enhance model expressiveness.…”

Section: Introductionmentioning

confidence: 99%

EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm

Zhang¹,

Li²,

Wang³

et al. 2022

Preprint

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Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation. Then inspired by effective EA variants, we propose a novel pyramid EATFormer backbone that only contains the proposed EA-based Transformer (EAT) block, which consists of three residual parts, i.e., Multi-Scale Region Aggregation (MSRA), Global and Local Interaction (GLI), and Feed-Forward Network (FFN) modules, to model multi-scale, interactive, and individual information separately. Moreover, we design a Task-Related Head (TRH) docked with transformer backbone to complete final information fusion more flexibly and improve a Modulated Deformable MSA (MD-MSA) to dynamically model irregular locations. Massive quantitative and quantitative experiments on image classification, downstream tasks, and explanatory experiments demonstrate the effectiveness and superiority of our approach over State-Of-The-Art (SOTA) methods. E.g., our Mobile (1.8M), Tiny (6.1M), Small (24.3M), and Base (49.0M) models achieve 69.4, 78.4, 83.1, and 83.9

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Differential evolution algorithm with multi-population cooperation and multi-strategy integration

Cited by 53 publications

References 48 publications

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

International trade and finance exploration using network model of computer trade platform

EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm

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