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COMPLETE AND APPROXIMATE METHODS FOR OFF-LINE PLACEMENT OF HARDWARE TASKS ON RECONFIGURABLE DEVICES

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2013年第2期22卷 1-1页

作者： Belaid, Ikbel Ouni, Bassem Muller, Fabrice Benjemaa, Maher Univ Nice Sophia Antipolis Elect Antennas & Telecommun Lab F-06560 Valbonne France Natl Sch Engn Sfax Res Unit ReDCAD Sfax Tunisia

With the advent of run-time partial reconfiguration, the most recent reconfigurable devices support reconfiguring hardware tasks individually, without interrupting the remaining tasks running on the same device. While the concept of run-time partial reconfiguration increases performance and resource utilization, it also leads to resource wastage, high configuration overhead and complex allocation situations of hardware tasks on reconfigurable devices. Many on-line and off-line methods for hardware task placement have been proposed for such reconfigurable devices to enhance placement quality expressed by fragmentation rate, the amount of task rejection and a few of them also estimate configuration overhead. However, these works treat each criterion individually and therefore do not reflect the overall metrics of placement quality. Hardware task placement is a multi-objective combinatory optimization problem. In this paper, we investigate the problem of off-line placement of hardware tasks in partially reconfigurable devices and we present a new three-level resource management that is based on two methods, i.e., a complete analytic method: the formulation into mixed integer programming, and an approximate iterative method: the bees algorithm. For both methods, the placement quality is measured by the rate of resource efficiency and by the amount of configuration overhead. Experiments demonstrate that the analytic method provides better resource efficiency than the bees algorithm by 33% and attains 15% of gain in configuration overhead.

关键词： Hardware task placement heterogeneous FPGA mixed integer programming bees algorithm partial run-time reconfiguration resource efficiency configuration overhead

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Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony algorithm: A Case Study of Grid-Connected PV System Power Prediction

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017年第1期2017卷 1-14页

作者： Gao, Xiang-ming Yang, Shi-feng Pan, San-bo Anyang Normal Univ Sch Phys & Elect Engn Anyang 455000 Peoples R China Tianjin Univ Sci & Technol Coll Elect Informat & Automat Tianjin 300222 Peoples R China

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMDand ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

关键词： SUPPORT vector machines bees algorithm PHOTOVOLTAIC power generation TIME series analysis PREDICTION models HILBERT-Huang transform CASE studies

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Metaheuristics for the multi-task simultaneous supervision dual resource-constrained scheduling problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 96卷 104004-104004页

作者： Akbar, Muhammad Irohara, Takashi Sophia Univ Fac Sci & Technol Green Sci & Engn Div Chiyoda Ku 7-1 Kioi Cho Tokyo 1028554 Japan Bandung Inst Technol Dept Ind Engn Jl Ganesha 10 Bandung 40132 Indonesia

This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem that deals with machine assignment, job sequencing, operator allocation, and task sequencing. Setup and unloading must be scheduled to operators, and they are allowed to leave machines while processing jobs. Earlier research on MTSSDRC developed a permutation-based genetic algorithm (PGA) with a specific decoding scheme, namely DSE, to solve the problem. Many previous studies succeed in solving scheduling problems by modifying well-known metaheuristic techniques. Therefore, we are inspired by this to explore further modifications to particular metaheuristics. The first contribution of the present study lies in the development of new decoding schemes that can perform better than the existing option. Five new decoding schemes are considered. Two of those schemes, namely DS2 and DS4, perform significantly better than DSE, reaching 6% relative deviation. DS4 is superior in terms of solution quality, but DS2 can run eight times faster. Another contribution is the development of six modified metaheuristics that are implemented for the MTSSDRC problem: tabu search, simulated annealing, particle swarm optimization, bees algorithm (BA), artificial bee colony, and grey wolf optimization. The performance of these metaheuristics is compared with that of the PGA. The results show that the PGA and BA are consistently superior for medium- and large-sized problems. The BA is more promising in terms of solution quality, but the PGA is faster.

关键词： MTSSDRC Decoding schemes Metaheuristics bees algorithm Permutation-based genetic algorithm

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Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第6期11卷 180-192页

作者： Liang, Yuchen Wang, Yuqi Gu, Chengyi Tang, Jie Pang, Xianjuan Jiangsu Univ Sch Mech Engn 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn 1040 Heping Ave Wuhan 430070 Hubei Peoples R China Jiangsu Haiyu Machinery Co Ltd 8 Yingbin Rd Taizhou 225714 Jiangsu Peoples R China Henan Univ Sci & Technol Natl United Engn Lab Adv Bearing Tribol 263 Kaiyuan Ave Luoyang 471000 Henan Peoples R China

Predicting cutting tool remaining life is important to sustainable machining. Accurate wear assessment improves efficiency, reduces waste , and lowers costs by minimizing tool failure . Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilizing multi-source data to address these challenges effectively. The key innovations include the following: (1) the proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardized data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life;best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite element analysis is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimization mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64%, and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in improving the operational efficiency and reliability of manufacturing systems is validated through the integration of advanced data analytics and deep learning techniques.

关键词： fog computing adaptive prognosis cutting tool life prediction multi-source data fusion bees algorithm

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Cuckoo Search algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015年第1期2015卷 374873-374873页

作者： Wang, Jie-sheng Li, Shu-xia Song, Jiang-di Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114044 Peoples R China

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

关键词： SEARCH algorithms MATHEMATICAL optimization VELOCITY bees algorithm PARTICLE swarm optimization

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Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2017年第Pt.1期2017卷 1-15页

作者： Gokkus, Umit Yildirim, Mehmet Sinan Aydin, Metin Mutlu Manisa Celal Bayar Univ Dept Civil Engn TR-45140 Muradiye Manisa Turkey Akdeniz Univ Dept Civil Engn TR-07158 Antalya Turkey

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

关键词： TRAFFIC estimation HARBORS FORECASTING NEURAL networks (Computer science) bees algorithm

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A Hybrid Artificial Bee Colony algorithm for the Service Selection Problem

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2014年第1期2014卷 1-13页

作者： Zhang, Changsheng Zhang, Bin Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm called h-ABC is proposed, which incorporates the ant colony optimizationmechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strategies are designed for different groups of bees in the swarm. Finally, this approach is evaluated experimentally using different standard real datasets and synthetically generated datasets and compared with some recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solutions.

关键词： bees algorithm ANT algorithms SEARCH algorithms MATHEMATICAL optimization ADAPTIVE control systems

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Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

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JOURNAL OF APPLIED MATHEMATICS 2014年第unknown期000卷 1-20页

作者： Ma, Lianbo Hu, Kunyuan Zhu, Yunlong Niu, Ben Chen, Hanning He, Maowei Chinese Acad Sci Shenyang Inst Automat Dept Informat Serv & Intelligent Control Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Hefei Inst Intelligent Machines Hefei 230031 Peoples R China

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

关键词： DISCRETE systems CONTINUOUS functions MATHEMATICAL optimization HIERARCHICAL Bayes model bees algorithm

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Artificial Bee Colony algorithm with Time-Varying Strategy

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2015年第1期2015卷 17-17页

作者： Qin, Quande Cheng, Shi Zhang, Qingyu Li, Li Shi, Yuhui Shenzhen Univ Coll Management Dept Management Sci Shenzhen 518060 Peoples R China Shenzhen Univ Res Inst Business Analyt & Supply Chain Managemen Shenzhen 518060 Peoples R China Univ Nottingham Ningbo China Div Comp Sci Ningbo 315100 Zhejiang Peoples R China Univ Nottingham Ningbo China Int Doctoral Innovat Ctr Ningbo 315100 Zhejiang Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

关键词： bees algorithm TIME-varying systems SWARM intelligence NONLINEAR systems PROBLEM solving

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Performance Analysis of IoT-Based Health and Environment WSN Deployment

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SENSORS 2020年第20期20卷 5923.-5923.页

作者： Shakeri, Maryam Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi Islam, S. M. Riazul KN Toosi Univ Technol Fac Geodesy & Geomat Engn Geoinformat Technol Ctr Excellence Tehran 19697 Iran Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the bees algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

关键词： wireless sensor network deployment health and environment applications IoT bees algorithm PSO algorithm coverage lifetime Minimum Spanning Tree

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