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Combination of reinforcement learning and bee algorithm for controlling two-link arm with six muscle: simplified human arm model in the horizontal plane

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2020年第1期43卷 135-142页

作者： Rahatabad, Fereidoun Nowshiravan Rangraz, Parisa Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran

The aim of this study was to improve reinforcement learning algorithm by combining artificial bee colony algorithm. The traditional method of reinforcement learning algorithm has a very low convergence rate due to random choices. An ant algorithm will help to make random choices in reinforcement learning more appropriate. This hybrid algorithm called the bee colony reinforcement (BCR) algorithm. The tip of the arm must reach a predetermined purpose by BCR algorithm. The results show that the BCR algorithm in the model has been able to reduce the time to reach the goal than the reinforcement learning algorithm (In average 12 steps faster). Also, the path for reaching the goal in the BCR algorithm was far more direct and shorter than the reinforcement learning algorithm. This method also detects the optimal path towards the goal.

关键词： Reinforcement learning algorithm Human arm modeling artificial bee colony algorithm Optimization

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Two heuristics for the rainbow spanning forest problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第3期285卷 853-864页

作者： Ghoshal, Sudishna Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

A rainbow spanning forest (say RF) of a given connected, undirected and edge-colored graph (G) is a spanning forest of a set of rainbow components, where a rainbow component is a tree whose edges have different colors. The rainbow spanning forest problem (RSFP) of G aims to find a RF of G with the minimum number of rainbow components. The RSFP which is recently introduced is NP-hard on general graphs and trees, but finds its applications in many situations in which one requires to distinguish between different types of connections in network. To the best of our knowledge, there exists only one paper (Carrabs et al. 2018b) that presents a problem-specific heuristic called greedy algorithm (say GH) and a multi-start scheme embedding this GH (say MSGH) in order to further improve the results. This paper presents a novel and fast problem-specific heuristic Heu_RSF. With the intent of improving solution quality at the cost of higher computational time, we present an artificial bee colony algorithm (ABC_RSFP) for the RSFP. Main components of ABC_RSFP such as randomized version of Heu_RSF in solution initialization and grouping-based neighborhood strategies in conjunction with the use of Heu_RSF as a repair operator help in making ABC_RSFP more effective framework. On a set of benchmark instance scenarios, experimental results suggest that Heu_RSF is effective particularly on instances of larger scenarios with higher density and is computationally much faster than GH and MSGH. ABC_RSFP outperforms MSGH on almost all instance scenarios and shows its effectiveness in terms of computational time. (C) 2020 Elsevier B.V. All rights reserved.

关键词： (O) Combinatorial optimization Rainbow spanning forest problem Problem-specific heuristic artificial bee colony algorithm Neighborhood strategies

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Investigation of Low Thrust Optimal Orbital Transfer from LEO to GEO Considering Circular Orbits

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JOURNAL OF THE ASTRONAUTICAL SCIENCES 2020年第1期67卷 77-97页

作者： Fakoor, Mahdi Sadeghi, Sahar Bakhtiari, Majid Univ Tehran Fac New Sci & Technol Tehran Iran Iran Univ Sci & Technol Sch New Technol Tehran Iran

In the present research, optimal control problem for low thrust spacecraft orbital transfer is investigated by combination of analytical method and artificial bee colony (ABC) algorithm, which is a population-based method. Spacecraft trajectory optimization is defined for orbital transfer from Low Earth Orbit (LEO) to Geosynchronous Earth Orbit (GEO). In the proposed analytical method co-state variables are simplified by employing concepts of optimal control and specific conditions which are considered for the problem, consequently, an adjoint differential equation is added to set of differential equations and these equations are solved simultaneously that result in optimum transfer trajectory. Dynamic modeling of the problem is represented based on Edelbaum equations and trajectory design is carried out considering circular orbits. Optimization problem is defined for different kinds of objective functions such as minimum time, minimum control effort, minimum time with minimum control effort as well as both minimum time and minimum control effort considering different optimal weighted coefficients. These coefficients are searched by means of artificial bee colony algorithm in weighted minimum time minimum effort scheme. Initial values of control variable and terminal time for all defined objective functions are foraged through artificial bee colony algorithm. The rate of changes of the states and control variables are obtained and depicted during orbit transfer. The obtained results demonstrate considerable convergence as well as sufficient accuracy.

关键词： Low thrust Optimal control Analytical method Trajectory design artificial bee colony algorithm

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A new methodology for optimization and prediction of rate of penetration during drilling operations

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ENGINEERING WITH COMPUTERS 2020年第2期36卷 587-595页

作者： Zhao, Yanru Noorbakhsh, Amin Koopialipoor, Mohammadreza Azizi, Aydin Tahir, M. M. Shenzhen Univ Coll Civil Engn Shenzhen 518060 Peoples R China Amirkabir Univ Technol Dept Petr Engn Tehran 15914 Iran Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran German Univ Technol Engn Dept Muscat Oman Univ Teknol Malaysia UTM Construct Res Ctr ISIIC Fac Civil Engn Johor Baharu 81310 Johor Malaysia

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R-2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.

关键词： Rate of penetration Optimization Prediction artificial neural network artificial bee colony algorithm

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Proposal for training a Cellular Neural Network using a Hybrid artificial bee colony and Nelder-Mead algorithms

Proposal for training a Cellular Neural Network using a Hybr...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： J.J. Morales-Romero M.A. Reyes-Barranca L.M. Flores-Nava E.R. Espinosa-García CINVESTAV-IPN Mexico City Mexico

ISBN: (数字)9781728189871

ISBN: (纸本)9781728189888

In this work, we propose a methodology for training a Cellular Neural Network based on the artificial bee colony algorithm and the Nelder-Mead algorithm. The results of this proposal are compared with training using only the artificial bee colony algorithm and with the Nelder-Mead algorithm, also with related works. The Cellular Neural Network is directed and trained for image processing, and the selected tasks for the performance evaluation of the Cellular Neural Network are edge detection and noise remover.

关键词： Image processing artificial bee colony algorithm Training Image edge detection Computer architecture Optimization Microprocessors

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Metaheuristic algorithms Applied to Color Image Segmentation on HSV Space

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JOURNAL OF IMAGING 2022年第1期8卷 6-6页

作者： Giuliani, Donatella Univ Bologna Sch Econ Management & Stat I-40126 Bologna Italy

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the artificial bee colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes' rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired artificial bee colony algorithm is performed for edge extraction.

关键词： color image segmentation color image processing metaheuristic algorithms firefly algorithm artificial bee colony algorithm

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Gradient boosted regression model for the degradation analysis of prismatic cells

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 144卷 106494-106494页

作者： Wang, Fu-Kwun Mamo, Tadele Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan

Developing an accurate predictive model in a battery management system is a challenging task. Tree-based models are widely used to deal with non-linear problems because of their relative ease and prediction capabilities. We propose a gradient boosted regression (GBR) model with the artificial bee colony (ABC) algorithm to analyze the capacity degradation of prismatic cells. The ABC algorithm is used to obtain the optimal parameters of the GBR model. The proposed model is validated by six prismatic cells. The results show that the proposed model provides better prediction accuracy than long short-term memory (LSTM), empirical mode decomposition-based LSTM (EMD-LSTM), Elman-based LSTM and random forest regression (RFR) models. Besides, the effect of optimal hyperparameters for LSTM and proposed models is provided. The average calculation time including the time to find optimal model parameters for all datasets is 2.05 min. For four unseen datasets, the mean absolute percentage errors (MAPE) of the proposed model are obtained as 0.70%, 0.62%, 0.87%, and 0.46%. The results show that our proposed model can reliably predict the capacity degradation of prismatic cells.

关键词： Remaining useful life Prismatic cell Gradient boosted regression artificial bee colony algorithm

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An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

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OPEN GEOSCIENCES 2020年第1期12卷 580-597页

作者： Hamzeh, Mohamad Karimipour, Farid Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Dept GIS Tehran *** Iran Univ Tehran Dept GIS Tehran Iran

An inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts' knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80-90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

关键词： optimization artificial bee colony algorithm outranking fuzzy ELECTRE petroleum potential Red River petroleum system Williston Basin

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Spiking neural network approaches PCA with metaheuristics

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ELECTRONICS LETTERS 2020年第10期56卷 488-+页

作者： Enriquez-Gaytan, J. Gomez-Castaneda, F. Flores-Nava, L. M. Moreno-Cadenas, J. A. Cinvestav IPN Dept Elect Engn Mexico City DF Mexico

This Letter presents meaningful results that demonstrate the reduction of dimensionality by spiking neural networks (SNNs) on benchmarking data. This experimental scheme includes metaheuristics, namely, the artificial bee colony algorithm (ABC algorithm) for finding optimal conductance values in the SNNs. Therefore, the objective function in the used ABC algorithm leads the SNNs to compute the principal component analysis (PCA), efficiently. The eigendecomposition of the information drawn by the SNNs in the training phase is the base of the formulated objective function. In these experiments, the Izhikevich model represents the spiking neurons, which have biological plausibility with parameters for reproducing a uniform firing rate. The visualisation of clusters in the 3D PCA space, whose sample values are compared with the PCA function in Matlab, is also shown;this comparison demonstrates an acceptable error in the MSE sense.

关键词： principal component analysis neural nets mean square error methods neurophysiology optimisation principal component analysis SNNs formulated objective function spiking neurons PCA function neural network metaheuristics meaningful results experimental scheme artificial bee colony algorithm ABC algorithm optimal conductance values

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Two hybrid metaheuristic approaches for the covering salesman problem

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NEURAL COMPUTING & APPLICATIONS 2020年第19期32卷 15643-15663页

作者： Pandiri, Venkatesh Singh, Alok Rossi, Andre Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 India Univ Paris 09 PSL LAMSADE UMR CNRS 7243 Pl Marechal de Lattre de Tassigny F-75775 Paris 16 France Univ Angers LERIA EA CNRS 2645 F-49000 Angers France

This paper addresses the covering salesman problem (CSP), which is an extension of the classical traveling salesman problem (TSP). Given a set of cities and a coverage radius associated with each one of them, the CSP seeks a Hamiltonian cycle over a subset of cities such that each city not in the subset is within the coverage radius of at least one city in the subset and that has minimum length among all Hamiltonian cycles over such subsets. To solve this problem, one has to deal with the aspects of subset selection and permutation. The CSP finds application in emergency and disaster management and rural healthcare. This paper presents two hybrid metaheuristic approaches for the CSP. The first approach is based on the artificial bee colony algorithm, whereas the latter approach is based on the genetic algorithm. Both the approaches make use of several new first improvement or best improvement based local search strategies defined over various neighborhood structures. Computational results on a wide range of benchmark instances demonstrate the effectiveness of the proposed approaches. We are able to improve the best known solution values on majority of the large instances.

关键词： Covering salesman problem Traveling salesman problem Heuristic Genetic algorithm artificial bee colony algorithm

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