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Placement of Optical Sensors in 3D Terrain Using a Bacterial evolutionary Algorithm

SENSORS 2022年第3期22卷 1161-1161页

作者： Kovacs, Szilard Bolemanyi, Balazs Botzheim, Janos Budapest Univ Technol & Econ Fac Mech Engn Dept Mech Opt & Mech Engn Informat 4-6 Bertalan Lajos St H-1111 Budapest Hungary Eotvos Lorand Univ Dept Artificial Intelligence Fac Informat Pazmany P Setany 1-A H-1117 Budapest Hungary

This paper proposes an optimization framework for terrain large scale optical sensor placement to improve border protection. Compared to the often used, maximal coverage of an area approach, this method minimizes the undetected passages in the monitored area. Border protection is one of the most critical areas for sensor placement. Unlike traditional border protection solutions, we do not optimize for 2D but for 3D to prevent transit. Additionally, we consider both natural and built environmental coverings. The applied environmental model creates a highly inhomogeneous sensing area for sensors instead of the previously used homogeneous one. The detection of each sensor was provided by a line-of-sight model supplemented with inhomogeneous probabilities. The optimization was performed using a bacterial evolutionary algorithm. In addition to maximizing detection, minimizing the number of the applied sensors played a crucial role in design. These two cost components are built on each other hierarchically. The developed simulation framework based on ray tracing provided an excellent opportunity to optimize large areas. The presented simulation results prove the efficiency of this method. The results were evaluated by testing on a large number of intruders. Using sensors with different quantities and layouts in the tested 1x1x1 km environment, we reduced the probability of undetected intrusion to below 0.1% and increased the probability of acceptable classification to 99%.

关键词： sensor placement route detection evolutionary computing

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Online optimization of adjustable settings for agricultural machinery assisted by digital twin

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 227卷

作者： Guo, Dafang Wang, Linze Du, Yuefeng Wu, Zhikang Zhang, Weiran Zhi, Qiao Ma, Ruofei China Agr Univ Coll Engn Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

To address the complex and variable agricultural production, the adjustable settings on agricultural machinery have become increasingly numerous. However, determining the most applicable set of settings from thousands of possible combinations has emerged as a new challenge, one that is difficult to achieve through traditional experience-based decision-making and feedback control. This study analyzed the characteristics of the online optimization problem for adjustable settings in agricultural machinery, framing it as a single-objective cost optimization problem with a continuous feasible region and multi-modality. By introducing Digital Twin (DT) technology, a DT-assisted online optimization method (DTAOO) is proposed to search for the optimal set of setting. Specifically, DTAOO consists of two parts. One part involves the building of the DT, using an ensemble modeling combined with data augmentation to quickly establish and reconstruct the DT based on small sample data collected from physical space. The other part is the DT-assisted evolutionary algorithm (DTAEA), which employs the DT to predictively evaluate candidate solutions in a virtual space. This assists the evolutionary algorithm in searching for the most promising candidate solutions. In numerical experiments, the performance of DTAOO was evaluated through a series of benchmark problems and compared with other representative peer algorithms. Experimental results show that DTAOO achieved better results than peer algorithms on some complex benchmark problems. On multi-peak benchmark tests with uncertainty, DTAOO demonstrated a significant advantage. By applying DTAOO to optimize the settings related to the threshing process of a corn combine harvester, the grain breakage rate was reduced and working efficiency was improved, demonstrating the practical applicability of DTAOO. This study contributes to searching the optimal set of adjustable settings for agricultural machinery in complex production environment

关键词： Data driven Machine learning evolutionary computing Decision support Agricultural cybernetics

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Semantic Cells: Evolutional process for item sense disambiguation

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Procedia Computer Science 2024年 246卷 4843-4852页

作者： Yukio Ohsawa Dingming Xue Kaira Sekiguchi The University of Tokyo Tokyo Japan

Previous models for learning the semantic vectors of items and their groups, such as words, sentences, nodes, and graphs, using distributed representation have been based on the assumption that the basic sense of an item corresponds to one vector composed of dimensions corresponding to hidden contexts in the target real world, from which multiple senses of the item are obtained by conforming to lexical databases or adapting to the context. However, there may be multiple senses of an item, which are hardly assimilated and change or evolve dynamically following the contextual shift even within a document or a restricted period. This is a process similar to the evolution or adaptation of a living entity with/to environmental shifts. Setting the scope of disambiguation of items for sensemaking, the author presents a method in which a word or item in the data embraces multiple semantic vectors that evolve via interaction with others, similar to a cell embracing chromosomes crossing over with each other. We obtained a preliminary result: the role of a word that evolves to acquire the largest or lower-middle variance of semantic vectors tends to be explainable by the author of the text.

关键词： evolutionary computing word-sense disambiguation item-sense disambiguation bio-inspired computation

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Evolving masked low-rank transformer for long text understanding

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APPLIED SOFT computing 2024年 152卷

作者： Liu, Chenjing Chen, Xiangru Lin, Jie Hu, Peng Wang, Junfeng Geng, Xue Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore

Long sequence text processing is time-consuming owing to the ultra-large-scale self-attention computing. Recent advances demonstrate the attention in transformer can be accelerated by redundancy removal, and there are various sparse variants for attention in large sequences are proposed, which leads to state-of-the-art performance on language and vision task. Low-rank method achieve outstanding success in the field of efficient transformer. The dynamic token sparsification is efficiently time-saving and cost-saving, which can be easily extended to prune redundant spans and to yield semantic features. evolutionary algorithm is attractive for selecting hyperparameter which is of significant importance in effectiveness. Motivated by these works, we propose an efficient transformers model, termed EMLT, to alleviate time and cost without sacrificing the accuracy. EMLT effectively combines strengths of Low-rank transformers, dynamic token sparsification and evolutionary algorithm to ulteriorly cut redundant token and meanwhile maintains the original precision, which can achieve a linear memory and time complexity. We compress transformer in three stages. Firstly, sliding window is validated as local attention to capture fine-grained dependency semantics. After that, low -rank approximation of attention matrix is applied as global attention to store long-range dependency semantics, and aggregated with local attention. On this basis, we consistently prune redundant token in accordance with importance score to further sparse the attention operation. Finally, evolutionary algorithm is utilized to optimize the hyper-parameters of every layer. The results of comprehensive experiments and analysis show that our method can rival others on accuracy, and outperforms others on efficiency by a large margin in terms of the computational complexity.

关键词： evolutionary computing Efficient transformer Genetic algorithm Natural language processing Neural architecture search

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Stock price prediction using intelligent models, Ensemble Learning and feature selection 2

Stock price prediction using intelligent models, Ensemble Le...

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2nd International Conference on Distributed computing and High Performance computing (DCHPC)

作者： Nezhad, Mohammad Taghi Faghihi Rezaei, Mahdi Payame Noor Univ Dept Informat Technol Fac Engn Tehran Iran Univ Qom Fac Engn Qom Iran

ISBN: (纸本)9781665496728

The use of artificial intelligence-based models have shown that the stock market is predictable despite its uncertainty and unstable nature. The most important challenge of the proposed models in the stock market is the accuracy of the results and increasing the forecasting efficiency. To overcome this challenge, this paper employs ensemble learning (EL) model using intelligence-based learners and metaheuristic optimization methods to maximize the improvement of forecasting performance. The multiplicity of inputs in the prediction model reduces the speed of execution and increases complexity. The proposed model, with feature selection, increases the accuracy and use as a real-time model. Genetic algorithm (GA) and particle swarm optimization (PSO) technique are used to optimize the aggregation results of the base learners. The evaluation results of stock market dataset show that the proposed model can overcome the market fluctuations and can be used as a reliable model.

关键词： Ensemble learning Real time model Prediction of stock price evolutionary computing artificial neural networks

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Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第1期54卷 711-754页

作者： Machado, Marcelo de Oliveira Costa Bravo, Natalie Ferraz Silva Martins, Andre Ferreira Bernardino, Heder Soares Barrere, Eduardo de Souza, Jairo Francisco Fed Univ State Rio de Janeiro Rio De Janeiro Brazil Univ Fed Juiz de Fora Juiz De Fora MG Brazil

The presentation of learning materials in a sequence, which considers the association of students' individual characteristics with those of the knowledge domain of interest, is an effective learning strategy in online learning systems, especially if related to traditional approaches. However, this sequencing, called Adaptive Curriculum Sequencing (ACS), represents a problem that falls in the NP-Hard class of problems given the diversity of sequences that could be chosen from ever-larger repositories of learning materials. Thus, metaheuristics are usually employed to tackle this problem. This study aims to present a systematic review and mapping of the literature to identify, analyze, and classify the published solutions related to the ACS problem addressed by metaheuristics. We considered 61 studies in the mapping and 58 studies in the review from 2005 to 2018. Even though the problem is longstanding, it is still discussed, especially considering new modeling and used metaheuristics. In this sense, we emphasize the use of Swarm Intelligence and Genetic Algorithm. Moreover, we have identified that various parameters were considered for students and knowledge domain modeling, however, few student's intrinsic parameters have been explored in ACS literature.

关键词： Adaptive learning evolutionary computing Learning path Student modeling Knowledge domain modeling Intelligent tutoring systems Artificial intelligence in education

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Forecast of complex financial big data using model tree optimized by bilevel evolution strategy

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JOURNAL OF BIG DATA 2021年第1期8卷 1-13页

作者： Senoguchi, Junsuke Tokyo Univ Technol Dept Comp Sci Hachioji Tokyo Japan

If a decision tree is constructed through a series of locally optimal solutions, such as the Greedy method, overfitting to the data is likely to occur. In order to avoid overfitting, many previous research have attempted to collectively optimize the structure of a decision tree by using evolutionary computation. However, if attributes of each split and their thresholds are searched simultaneously, the evaluation function becomes intermittent;thus, optimization methods assuming continuous distribution cannot be used. In this study, in order to enable efficient search assuming continuous distribution even for complicated data that contains a lot of noise and extraordinary values, such as financial time series data, the inner level search that optimizes each threshold value collectively given a specific attribute for each split in a model tree and the outer level search that optimizes the attributes of each split were performed by separate evolutionary computing. As a result, we obtained high prediction accuracy that far exceeded the performance of the conventional method.

关键词： evolutionary computing Noisy function optimization Financial market Forecasting Model tree

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AN IMPROVED TOP-EYE TRAJECTORY ANOMALY DETECTION METHOD INTEGRATING SEMANTIC FEATURES

AN IMPROVED TOP-EYE TRAJECTORY ANOMALY DETECTION METHOD INTE...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhou, Yan Zhang, Cong Qin, Mengdou Zhang, Yeting Wang, Jiaqi Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Anomalous trajectory detection is an important research content in the field of trajectory data mining. The anomaly detection method based on Top-k evolving trajectory outlier detection (TOP-EYE) is an effective anomaly detection method for moving objects that adopts evolutionary computation to detect anomalies from two perspectives of density and direction. However, the TOP-EYE method only considers two factors of density and direction, ignoring the multi-dimensional semantic features of trajectory data. In order to improve the accuracy of anomaly detection of trajectory data, this paper proposes an improved TOP-EYE method integrating semantic features, which adds trajectory semantic features for anomaly trajectory detection based on TOP-EYE. Experiments show that the proposed improved method integrating semantic features can more accurately identify anomalous trajectories than TOP-EYE, which is helpful for mining potential trajectory outliers with anomalous semantic features.

关键词： trajectory anomaly detection evolutionary computing semantic feature

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Differential evolution outside the box

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INFORMATION SCIENCES 2021年 581卷 587-604页

作者： Kononova, Anna, V Caraffini, Fabio Back, Thomas Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands De Montfort Univ Inst Artificial Intelligence Leicester Leics England

This paper investigates how often the popular configurations of Differential Evolution gen-erate solutions outside the feasible domain. Following previous publications in the field, we argue that what the algorithm does with such solutions and how often this has to hap-pen is important for the overall performance of the algorithm and interpretation of results. Based on observations therein, we conclude that significantly more solutions than what is usually assumed by practitioners need to undergo some sort of 'correction' to conform with the definition of the problem's search domain. A wide range of popular Differential Evolution configurations is considered in this study. Conclusions are made regarding the effect the Differential Evolution components and parameter settings have on the distribu-tion of proportions of infeasible solutions generated in a series of independent runs. Results shown in this study suggest strong dependencies between proportions of generated infea-sible solutions and every aspect mentioned above. Further investigation of the distribution of proportions of generated infeasible solutions is required. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Differential evolution Infeasibility Constraints handling evolutionary computing Box-constrained problem Metaheuristic

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Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

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WIRELESS PERSONAL COMMUNICATIONS 2021年第3期116卷 1743-1764页

作者： Kalra, Mala Singh, Sarbjeet Natl Inst Tech Teachers Training & Res Chandigarh India Panjab Univ Univ Inst Engn & Technol Chandigarh India

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user's Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.

关键词： Cloud computing Energy aware scheduling Genetic algorithm Intelligent water drops algorithm Multi-objective optimization evolutionary computing

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