An adaptive regeneration framework based on search space adjustment for differential evolution

Sun, Gaoji; Li, Chunlei; Deng, Libao

doi:10.1007/s00521-021-05708-1

Cited by 103 publications

(46 citation statements)

References 62 publications

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“…Several kinds of natural hazards related environment problems has been solved by various research groups for sustainable management and utilization of natural resources (Pradhan and Kim, 2017;Jiang et al, 2018;Tsai et al, 2019;Wang et al, 2020;Xu et al, 2021) with the help of remote sensing (RS) technology (Han et al, 2019;Hu et al, 2020;Zhang et al, 2020c) and geographical information system (GIS) tool (Zuo et al, 2015;Xu et al, 2018;Zhu et al, 2019;Yang et al, 2020b) and widespread progress in the computational facilities (Chao et al, 2018;Zhang et al, 2018Zhang et al, , 2020bCao et al, 2020;Xu et al, 2020;Feng et al, 2020). A noteworthy support from the combination of RS and GIS technology gives efficiently solution in the several types of natural hazards related problems (Yang et al, 2018(Yang et al, , 2020aSun et al, 2021). Therefore, the geospatial technology, i.e., remote sensing and GIS has been providing high resolution multispectral satellite data and their optimal processing which is immensely help to analysis and solves several types of natural hazards related risk.…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Discussionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…e evolutionary game theory is used to study the evolutionary stable strategy of tacit knowledge sharing between organizations [19]. Although these literature studies are based on bounded rationality, in general, they only considered the internal factors such as knowledge sources, knowledge characteristics, technical means, networks, and how external incentive policies affect the choice of network tacit knowledge sharing behaviors but they lack discussion [20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Evolutionary Game Analysis of Knowledge Sharing in Low‐Carbon Innovation Network

Zheng

2021

Complexity

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Low-carbon technological innovation is the main means to develop a low-carbon economy, and network knowledge sharing and collaborative innovation is an effective model for the development of low-carbon technologies. First of all, this article adopts a decision-making experiment and evaluation laboratory method and interpretation structure model, combines the two methods, extracts the advantages of the two, and discards the shortcomings of the two, thus constructing a new optimized and upgraded interpretation structure model. We give methods to explore the main influencing factors of collaborative innovation of low-carbon technologies for online knowledge sharing. Based on the industrial network knowledge sharing and cooperation network environment, the network evolution game model of network knowledge sharing knowledge collaboration is constructed to study the rewards and punishments, the profit distribution rate, the knowledge potential difference, and the parameter pairing of the network knowledge sharing cooperation network structure in the process of network knowledge sharing and collaborative knowledge innovation. The influence of the network knowledge sharing cooperation strategy is obtained through simulation to change the size of the relevant parameters so that the network knowledge sharing cooperation agent chooses the network evolution game of the sharing strategy to realize the optimal evolutionary stable strategy. According to the simulation results, this article proposes suggestions from the following aspects, aiming to improve the overall knowledge synergy effect of the network knowledge sharing and cooperation network.

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“…at is, all pixel points in the image region contribute to the color features of the region, so the color features are not very sensitive to the changes of size and direction, etc. in the image region, and thus the color features are not very sensitive to the local changes of the target [24][25][26][27]. e response is not very sensitive to local changes in the target.…”

Section: Adaptive Scale Target Tracking Algorithmmentioning

confidence: 99%

[Retracted] Target Tracking Algorithm Based on Adaptive Scale Detection Learning

Yang

2021

Complexity

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In this paper, to better solve the problem of low tracking accuracy caused by the sudden change of target scale, we design and propose an adaptive scale mutation tracking algorithm using a deep learning network to detect the target first and then track it using the kernel correlation filtering method and verify the effectiveness of the model through experiments. The improvement point of this paper is to change the traditional kernel correlation filtering algorithm to detect and track at the same time and to combine deep learning with traditional kernel correlation filtering tracking to apply in the process of target tracking; the addition of deep learning network not only can learn more accurate feature representation but also can more effectively cope with the low resolution of video sequences, so that the algorithm in the case of scale mutation achieves more accurate target tracking in the case of scale mutation. To verify the effectiveness of this method in the case of scale mutation, four evaluation criteria, namely, average accuracy, cross-ratio accuracy, temporal robustness, and spatial robustness, are combined to demonstrate the effectiveness of the algorithm in the case of scale mutation. The experimental results verify that the joint detection strategy plays a good role in correcting the tracking drift caused by the subsequent abrupt change of the target scale and the effectiveness of the adaptive template update strategy. By adaptively changing the number of interval frames of neural network redetection to improve the tracking performance, the tracking speed is improved after the fusion of correlation filtering and neural network, and the combination of both is promoted for better application in target tracking tasks.

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An adaptive regeneration framework based on search space adjustment for differential evolution

Cited by 103 publications

References 62 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

[Retracted] Evolutionary Game Analysis of Knowledge Sharing in Low‐Carbon Innovation Network

[Retracted] Target Tracking Algorithm Based on Adaptive Scale Detection Learning

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