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A Q-learning guided search for developing a hybrid of mixed redundancy strategies to improve system reliability

RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年第1期236卷

作者： Hsieh, Tsung-Jung Natl Formosa Univ Ctr Gen Educ Yunlin Taiwan

Mixed redundancy strategies (MRSs) are based on leveraging a diverse combination of active and cold-standby components to improve system reliability. This study integrates three designed MRSs and employs them to develop a hybrid model to solve redundancy allocation problems (RAPs) and reliability-RAPs (RRAPs). The MRS best suited for a specific subsystem is difficult to determine. To resolve this issue, in the process of system reliability optimization, the state of limited resource utilization (e.g., cost, weight, and volume) in each sub-system facilitates the adoption of MRSs and is used as a learnable factor. To realize this learning process, Q-learning is used in this study to build a knowledge library (i.e., a Q-table) of MRS usage, where the Q-table guides the main optimization technique, the artificial bee colony algorithm (ABC), to expedite the convergence in searching for near-optimal solutions. For convenience, the Q-learning-guided ABC search method is abbreviated as QABCS, and the new MRSs obtained by QABCS are called Q-mixed. The experimental results show that Q-mixed not only improves system reliability but also reveals the preferences of each subsystem for the MRSs.

关键词： Q-learning Mixed redundancy strategy Reliability optimization Markov model artificial bee colony algorithm

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artificial bee colony optimization-based weighted extreme learning machine for imbalanced data learning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第3-Sup期22卷 S6937-S6952页

作者： Tang, Xiaofen Chen, Li Northwest Univ Sch Informat Sci & Technol Xian 710127 Shaanxi Peoples R China Ningxia Univ Sch Informat Engn Yinchuan 750021 Peoples R China

The imbalanced datasets are common in real-world application and the problem of imbalanced dataset affect classification performance of many standard learning approaches. To address imbalanced datasets, a weighted extreme learning machine (WELM) solving the L2-regularized weighted least squares problem is presented to avoid the generation of an over-fitting model and obtain better generalization ability compared with ELM. However, the weight generated according to class distribution of training data leads to lack of finding optimal weight with good generalization performance and the randomness of input weight and hidden biases of network makes the algorithm produce suboptimal classification model. In this paper, a weighted extreme learning machine based on hybrid artificial bee colony (HABC) is proposed to obtain better performance than WELM, in which input weights and hidden bias of WELM and the weight assigned to training samples are optimized by the hybrid artificial bee colony algorithm. HABC combines the diversities of the perturbed parameter vectors of differential evolution with the best solution information of the artificial bee colony effectively. In the empirical study, different class imbalance data handling methods including four WELM-based methods, weighted support vector machine, four ensemble methods which combine data sampling and the Bagging or Boosting are compared with our method. The experimental results on 15 imbalanced datasets show that the proposed method outperforms most methods, which indicates its superiority.

关键词： artificial bee colony algorithm Weighted extreme learning machine Imbalanced data learning Single hidden layer feed-forward networks

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Comparison between ABC and ACO: Tunning of On-Off MPPT for wind systems

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WIND ENGINEERING 2021年第6期45卷 1601-1612页

作者： Hannachi, Marwa Elbeji, Omessaad Benhamed, Mouna Sbita, Lassaad Natl Engn Sch Gabes Dept Elect Engn PEESE Proc Energet Environm & Elect Syst Res Lab Gabes 6029 Tunisia

In this work, a comparative study between two optimization algorithms for On-Off MPPT (Maximum Power Point Tracking) in wind power systems will be presented. The two optimizers considered in this paper are: the artificial bee colony algorithm (ABC) and the ant colony algorithms (ACO). Both of these optimization techniques are formulated to minimize different performance such as Integral Absolute Error (IAE) to determine optimal PI regulator values. In order to improve the performance and robustness properties of the proposed PI, the two tuning mechanisms are used in the optimization part. The system is modeled and tested under MATLAB/SIMULINK environment. The comparison of the performances, either by the test function or by MPPT, between these two optimizers shows the efficiency and superiority of the ABC-based approach proposed in terms of the qualities of the solution obtained, the speed of convergence, and the simplicity compared to ACO.

关键词： Wind turbine permanent magnet synchronous generator optimization artificial bee colony algorithm ant colony algorithms On-Off control PI regulator maximum power point tracking

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Optimal torque maximum power point technique for wind turbine: Proportional-integral controller tuning based on particle swarm optimization

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WIND ENGINEERING 2021年第2期45卷 337-350页

作者： Hannachi, Marwa Elbeji, Omessaad Benhamed, Mouna Sbita, Lassaad Natl Engn Sch Gabes Photovolta Wind & Geothermal Syst Res Unit Yepartment Elect Engn Zrig Gabes 6029 Tunisia

This article presents the problem of the energy system optimization for wind generators. The goal of this work is to maximize power extraction for a permanent magnet synchronous generator-based wind turbine with maximum power point technique. This goal is achieved using a proportional-integral controller for optimal torque tuning with the particle swarm optimization algorithm. In order to indicate the effectiveness and superiority of the particle swarm optimization algorithm-based proposal, a comparison with the genetic algorithm and the artificial bee colony algorithm is studied. The system is modeled and tested under MATLAB/Simulink environment. Simulation results validate the advantages of the designed particle swarm optimization-tuned proportional-integral controller compared to P&O and the proportional-integral controller manually in terms of performance index.

关键词： Wind turbine genetic algorithm artificial bee colony algorithm particle swarm optimization algorithm optimum torque maximum power point technique proportional-integral controller

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Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

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SENSORS 2023年第22期23卷 9277-9277页

作者： Jutharee, Wisanu Kaewkamnerdpong, Boonserm Maneewarn, Thavida King Mongkuts Univ Technol Thonburi Inst Field Robot Bangkok 10140 Thailand King Mongkuts Univ Technol Thonburi Fac Engn Biol Engn Program Human Factors Engn Res Grp Bangkok 10140 Thailand Yannix Co Ltd Bangkok 10150 Thailand Raina Robotech Co Ltd Bangkok 10150 Thailand

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures;there was no statistical difference. However, ABC yielded more reliable reconfigured gestures;there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints;hence, it could be used for joint failure recovery in real applications.

关键词： artificial bee colony algorithm bacteria foraging optimization algorithm bio-inspired computing failure recovery genetic algorithm joint reconfiguration humanoid robots redundant robots

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High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年第1期303卷 123176页

作者： Song, Shuai Wang, Qiaoyun Zou, Xin Li, Zhigang Ma, Zhenhe Jiang, Daying Fu, Yongqing Liu, Qiang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China Zhongyou BSS Qinhuangdao Petropipe Co Ltd Qinhuangdao 066004 Peoples R China Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne NE1 8ST England

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

关键词： Raman spectroscopy Blood glucose Bagging algorithm artificial bee colony algorithm Extreme Learning Machine

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Evaluation of green transit networks for tourism on Penghu Island

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 58卷

作者： Ke, Bwo-Ren Cai, Song-Han Natl Penghu Univ Sci & Technol Dept Elect Engn Magong Penghu Taiwan

Selecting modes of green transit for a transportation network can be challenging. In this study, individual and multiperson tourist travel itineraries for various green transit networks on Penghu Island in Taiwan were developed and optimized to minimize energy consumption, travel time, and construction costs;case studies involving various itineraries were conducted and compared. The green transit networks could enable low-carbon and ecofriendly tourism around Penghu Island. The individual itineraries were produced by identifying an optimal travel route for visiting various destinations in a predetermined order by using the available green transit and on foot. Itineraries for electric scooters alone were first developed, and these itineraries were compared with those for green transit networks comprising various combinations of electric buses, electric scooters, and shared bicycles. The itineraries were optimized using the genetic algorithm and artificial bee colony algorithm to minimize energy consumption, travel time, or construction costs. The network comprising electric scooters and shared bikes had the lowest energy consumption, and that comprising electric scooters and buses had the shortest travel time and the lowest construction costs.

关键词： Green carriers Traveling itinerary planning Optimization Genetic algorithm artificial bee colony algorithm

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A novel artificial bee colony optimiser with dynamic population size for multi-level threshold image segmentation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第1期13卷 32-44页

作者： Ma, Lianbo Wang, Xingwei Shen, Hai Huang, Min Northeastern Univ Coll Software Shenyang 110819 Liaoning Peoples R China Shenyang Normal Univ Coll Phys Sci & Technol Shenyang 110034 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimiser is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.

关键词： artificial bee colony algorithm population varying image segmentation

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Optimization of without SCM concrete exposed to seawater according to minimum cost and CO2 emissions: Sustainable design with ABC algorithm

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MATERIALS TODAY COMMUNICATIONS 2023年 35卷

作者： Kaplan, Gokhan Bayraktar, Oguzhan Yavuz Bayraktar, Tugrul Ataturk Univ Dept Civil Engn TR-25030 Erzurum Turkiye Kastamonu Univ Dept Civil Engn TR-37150 Kastamonu Turkiye Karabuk Univ Dept Ind Engn TR-78000 Karabuk Turkiye

The service life of marine concrete structures is relatively shorter due to various salts in seawater. In this context, assistance is obtained from the exposure classes in standards such as EN 206. In this study, an ABC-based (artificial bee colony) algorithm was developed that considers the CO2 emissions and cost analysis of concrete designed according to EN 206 standards. The number of studies involving algorithms in designs according to regulations and standards is very few in the literature. In addition, Supplementary Cementitious Materials (SCMs) are generally used in concretes exposed to sea water. This study investigated the physical, mechanical, durability, microstructure, cost and sustainability properties of concretes without SCM. This study produced nine concrete mixes with different cement dosages (260-300-340 kg/m3) and a water/cement ratio (0.45,0.55, and 0.65). The mixes were cured in normal water (NWC) and seawater (SWC). As the cement mixes' cement dosage increased, the water absorption and apparent porosity values decreased. Since the increase in the w/c ratio increased the capillary void content of the mixes, it enhanced the porosity and sorptivity values. While the 28d compressive strength of NWC-applied concretes varies between 31.3 and 39.8 MPa, the compressive strength of SWC-applied concrete varies between 30.7 and 36.8 MPa. After 80 wetting-drying cycles, the compressive strength of the concretes decreased below 20 MPa. The CO2 emission values of the concrete vary between 270 and 343 kg and their costs range between 17.9 and 21.5 $/m(3). According to the ABC algorithm, it was determined that cement dosage should be 260 kg/m(3), w/c ratio should be 0.45 for high compressive strength, low cost, and CO2 emission. As a result, it has been determined that a lower cost and more durable concrete design can be made if methods such as the ABC algorithm are combined with EN 206 standards.

关键词： Sustainability Durability Carbon footprint artificial bee colony algorithm Seawater attack

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artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 4837-4847页

作者： Asteris, Panagiotis G. Nikoo, Mehdi Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece Islamic Azad Univ Ahvaz Branch Young Researchers & Elite Club Ahvaz Iran

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence algorithm for optimization, which has already been successfully applied for the training of artificial neural network (ANN) models. This paper thoroughly explores the performance of the ABC algorithm for optimizing the connection weights of feed-forward (FF) neural network models, aiming to accurately determine one of the most critical parameters in reinforced concrete structures, namely the fundamental period of vibration. Specifically, this study focuses on the determination of the vibration period of reinforced concrete infilled framed structures, which is essential to earthquake design, using feed-forward ANNs. To this end, the number of storeys, the number of spans, the span length, the infill wall panel stiffness, and the percentage of openings within the infill panel are selected as input parameters, while the value of vibration period is the output parameter. The accuracy of the FF-ABC model is verified through comparison with available formulas in the literature. The results indicate that the artificial neural network, the weights of which had been optimized via the ABC algorithm, exhibits greater ability, flexibility and accuracy in comparison with statistical models.

关键词： artificial intelligence techniques artificial bee colony algorithm artificial neural networks Fundamental period Infilled frames Soft computing techniques

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