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Drilling rate of penetration prediction and optimization using response surface methodology and bat algorithm

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2016年 31卷 829-841页

作者： Moraveji, Mostafa Keshavarz Naderi, Meysam Amirkabir Univ Technol Dept Chem Engn Tehran Polytech 424 Hafez Ave Tehran 158754413 Iran Amirkabir Univ Technol Dept Petr Engn Tehran Polytech 424 Hafez Ave Tehran 158754413 Iran

Rate of penetration (ROP) prediction is crucial for drilling optimization because of its role in minimizing drilling costs. There are many factors, which determine the drilling rate of penetration. Typical factors include formation properties, mud rheology, weight on bit, bit rotation speed, type of bit, wellbore inclination, and bit hydraulics. In this paper, first, the simultaneous effect of six variables on penetration rate using real field drilling data has been investigated. Response surface methodology (RSM) was used to develop a mathematical relation between penetration rate and six factors. The important variables include well depth (D), weight on bit (WOB), bit rotation speed (N), bit jet impact force (IF), yield point to plastic viscosity ratio (Y-p/PV), 10 min to 10 s gel strength ratio (10MGS/10SGS). Next, bat algorithm (BA) was used to identify optimal range of factors in order to maximize drilling rate of penetration. Results indicate that the derived statistical model provides an efficient tool for estimation of ROP and determining optimum drilling conditions. Sensitivity study using analysis of variance shows that well depth, yield point to plastic viscosity ratio, weight on bit, bit rotation speed, bit jet impact force, and 10 min to 10 s gel strength ratio have the greatest effect on ROP variation respectively. Cumulative probability distribution of predicted ROP shows that the penetration rate can be estimated accurately at 95% confidence interval. In addition, study shows that by increasing well depth, there is an uncertainty in selecting the jet impact force as the best objective function to determine the effect of hydraulics on penetration rate. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Drilling Penetration rate Response surface methodology bat algorithm

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Global convergence analysis of the bat algorithm using a markovian framework and dynamical system theory

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 173-182页

作者： Chen, Si Peng, Guo-Hua He, Xing-Shi Yang, Xin-She Northwestern Polytech Univ Coll Nat & Appl Sci 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Xian Polytech Univ Coll Sci 19 Jinhua South Rd Xian 710048 Shaanxi Peoples R China Middlesex Univ Sch Sci & Technol London NW4 4BT England

The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： bat algorithm Global convergence Markov chain theory Dynamical system theory Parameters selection Optimization Swarm intelligence

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FPGA based hardware implementation of bat algorithm

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APPLIED SOFT COMPUTING 2017年 58卷 378-387页

作者： Ben Ameur, Mohamed Sadok Sakly, Anis Univ Monastir Lab Elect & Microelect Monastir Tunisia Univ Monastir Natl Engn Sch Monastir Res Unit ESIER Monastir Tunisia

Several meta-heuristics algorithms are used mainly for research of global optimal solutions for real and non-convex problems. Some of them are the Genetic algorithms (GA), Cuckoo Search algorithms (CS), Particle Swarm Optimization (PSO). Some algorithms have achieved satisfactory results but not all of them. Therefore, new algorithms give better optimization to solve many problems having continuous search space like bat algorithm (BA). Thats why we proposed a new hardware implementation on Field Programmable Gate Array (FPGA) of bat algorithm, it is a new proposed meta-heuristic for global optimization. The work presented in this article is designed to use new digital dedicated hardware solutions such as FPGAs that are available to generate a better implementation of bat algorithm. This circuit is well adapted to many applications because its material structure is molded with the requirements of calculations. Moreover the inherent parallelism of these new hardware solutions and their large computing capabilities makes the computing time negligible despite the complexity of these algorithms. (C) 2017 Published by Elsevier B.V All rights reserved.

关键词： bat algorithm Finite State Machine FPGA Parallele programming Optimisation

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Modified bat algorithm based on covariance adaptive evolution for global optimization problems

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SOFT COMPUTING 2018年第16期22卷 5215-5230页

作者： Shan, Xian Cheng, Huijin China Univ Petr Sch Sci Qingdao 266580 Peoples R China China Univ Petr Sch Econ & Management Qingdao 266580 Peoples R China

bat algorithm is a newly proposed swarm intelligence algorithm inspired by the echolocation behavior of bats, which has been successfully used in many optimization problems. However, due to its poor exploration ability, it still suffers from problems such as premature convergence and local optimum. In order to enhance the search ability of the algorithm, we propose an improved bat algorithm, which is based on the covariance adaptive evolution process. The information included in the covariance adaptive evolution diversifies the search directions and sampling distributions of the population, which is of great benefit to the search process. The proposed approaches have been tested on a set of benchmark functions. Experimental results indicate that the proposed algorithm obtains superior performance over the majority of the test problems.

关键词： bat algorithm Swarm intelligence Covariance adaptive evolution

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Novel chaotic bat algorithm for forecasting complex motion of floating platforms

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APPLIED MATHEMATICAL MODELLING 2019年 72卷 425-443页

作者： Hong, Wei-Chiang Li, Ming-Wei Geng, Jing Zhang, Yang Jiangsu Normal Univ Sch Comp Sci & Technol 101 Shanghai Rd Xuzhou 221116 Jiangsu Peoples R China Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Heilongjiang Peoples R China

This paper presents a model for forecasting the motion of a floating platform with satisfactory forecasting accuracy. First, owing to the complex nonlinear characteristics of a time series of floating platform motion data, a support vector regression model with a hybrid kernel function is used to simulate the motion of a floating platform. Second, the proposed chaotic efficient bat algorithm, based on the chaotic, niche search, and evolution mechanisms, is used to optimize the parameters of the hybrid kernel-based support vector regression model. Third, the ensemble empirical mode decomposition algorithm is utilized to decompose the original floating platform motion time series into a series of intrinsic mode functions and residuals. The ultimate forecasting results are obtained by summing the outputs of these functions. Subsequently, motion data for a real floating platform are used to evaluate the reliability and effectiveness of the proposed model. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Floating platform motion (FPM) forecasting Support vector regression (SVR) Hybrid kernel function Ensemble empirical mode decomposition bat algorithm Chaotic mapping mechanism

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Hedging Rules-Based Optimal Reservoir Operation Using bat algorithm

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WATER RESOURCES MANAGEMENT 2019年第13期33卷 4525-4538页

作者： Jamshidi, Javad Shourian, Mojtaba Shahid Beheshti Univ Fac Civil Fac Civil Water & Environm Engn Tech & Engn Coll Tehran Iran

In this study, optimal operation of a reservoir by incorporation of the hedging policy and the bat algorithm (BA) is investigated. The deficit in water supply by the dam is minimized as the objective function and the optimal monthly releases from the reservoir are determined and compared in three hedging-based operation rules. In the first rule, which has a single decision variable, a constant monthly release is considered for all 240 months of the operation period. In the second scenario, one fixed release is determined for each month of the year and is repeated in successive operating years which results 12 decision variables for the problem. In the third rule, all monthly releases are varied as the decision variables resulting 240 unknowns for the problem. The developed models are utilized for the Zhaveh reservoir in west of Iran. Results show that while BA is a suitable easy going algorithm to be applied for optimal reservoir operation planning, the amount of water deficit is lower when a higher degree of freedom is defined for the operating rules as the values for the objective function are 105.3, 102.8, and 80.5 for the first to the third scenarios, respectively. Afterward, the results using the hedging rules are compared with the standard operating policy (SOP) and it is found that the reservoir performance is more desirable in satisfying water demands when the hedging policy is applied.

关键词： Optimal reservoir operation Hedging Standard operating policy bat algorithm

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Prediction of seasonal bike rental counts using a GBM model optimized with bat algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2024年第4期39卷 2631-2642页

作者： Ileri, Kadir Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Elect & Elect Engn TR-10200 Balikesir Turkiye

To ensure effective resource allocation for urban bike demand, it is crucial to accurately predict shared bike rental counts. This prediction process was carried out using the Gradient Boosted Machine (GBM) method optimized with the bat algorithm (BA). To demonstrate the effectiveness of the proposed model, its performance was compared with different methods such as Decision Tree (DT), k -Nearest Neighbors (KNN), and Multi -Layer Perceptron (MLP). For this comparison, metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R -squared (R 2 ) were employed. The best results were achieved by BA-GBM with values of 1.8665 MAE, 2.9588 MSE, 8.7545 RMSE, and 0.9264 R 2 . Additionally, the features with the most and least impact on bike rental prediction were identified. The most influential features were found to be temperature and time of day, while the least influential features were snowfall and year.

关键词： bat algorithm Gradient Boosting Machine K-Nearest Neighbors Multi-Layer Perceptron Bike rental counts

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A novel bat algorithm based optimum tuning of mass dampers for improving the seismic safety of structures

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ENGINEERING STRUCTURES 2018年第Mar.15期159卷 89-98页

作者： Bekdas, Gebrail Nigdeli, Sinan Melih Yang, Xin-She Istanbul Univ Dept Civil Engn TR-34320 Istanbul Turkey Middlesex Univ Design Engn & Math London England

Metaheuristic algorithms are effective for optimization with diverse applications in engineering. The optimum tuning of tuned mass dampers is very important for seismic structures excited by random vibrations, and optimization techniques have been used to obtain the best performance for optimally tuned mass dampers. In this study, a novel optimization approach employing the bat algorithm with several modifications for the tuned mass damper optimization problem is presented. In the proposed method, the design variables such as the mass, period and damping ratio of tuned mass damper are optimized and different earthquake records are considered during the optimization process. The method is then applied to a ten-story civil structure and the results are then compared with the analytical methods and other methods such as genetic algorithms, particle swarm optimization, and harmony search. The comparison shows that the proposed method is more effective than other compared methods. Additionally, the robustness of the optimum results was evaluated. The proposed approach for optimizating tuned mass dampers via the bat algorithm is a feasible and efficient approach.

关键词： Tuned Mass Damper Optimization Metaheuristic algorithms bat algorithm Harmony search algorithm

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bat algorithm-Based Traffic Signal Optimization Problem 7th

Bat Algorithm-Based Traffic Signal Optimization Problem

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International Conference on Soft Computing for Problem Solving (SocProS)

作者： Srivastava, Sweta Sahana, Sudip Kumar Birla Inst Technol Dept Comp Sci Ranchi Bihar India

ISBN: (纸本)9789811315923;9789811315916

The need for the transport services and road network development came into existence with the development of civilization. In the present urban transport scenario with ever-mounting vehicles on the road network, it is very much essential to tackle network congestion and to minimize the overall travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as bi-level models based on Stackelberg game. The upper layer optimizes time spent in waiting at the traffic signals, and the lower layer solves stochastic user equilibrium. Soft computing techniques like genetic algorithms, ant colony optimization and many other biologically inspired techniques are proven to give good results for bi-level problems. Here, this work uses bat intelligence to solve the problem. The results are compared with the existing techniques.

关键词： bat algorithm Genetic algorithm Ant colony optimization

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bat algorithm for Kernel Computation in Fractal Image Reconstruction 19th

Bat Algorithm for Kernel Computation in Fractal Image Recons...

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19th Annual International Conference on Computational Science (ICCS)

作者： Galvez, Akemi Osaba, Eneko Del Ser, Javier Iglesias, Andres Toho Univ Dept Informat Sci Fac Sci Narashino Campus2-2-1 Miyama Funabashi Chiba 2748510 Japan Univ Cantabria Dept Appl Math & Comp Sci ETSI Caminos Canales & Puertos Avda Castros S-N Santander 39005 Spain TECNALIA Res & Innovat Derio 48160 Spain Univ Basque Country UPV EHU Bilbao Spain

ISBN: (纸本)9783030227449;9783030227432

Computer reconstruction of digital images is an important problem in many areas such as image processing, computer vision, medical imaging, sensor systems, robotics, and many others. A very popular approach in that regard is the use of different kernels for various morphological image processing operations such as dilation, erosion, blurring, sharpening, and so on. In this paper, we extend this idea to the reconstruction of digital fractal images. Our proposal is based on a new affine kernel particularly tailored for fractal images. The kernel computes the difference between the source and the reconstructed fractal images, leading to a difficult nonlinear constrained continuous optimization problem, solved by using a powerful nature-inspired metaheuristics for global optimization called the bat algorithm. An illustrative example is used to analyze the performance of this approach. Our experiments show that the method performs quite well but there is also room for further improvement. We conclude that this approach is promising and that it could be a very useful technique for efficient fractal image reconstruction.

关键词： Image processing Image reconstruction Affine kernel Fractal image bat algorithm

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