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Modeling rate of penetration using hybridization of artificial Neural Network and artificial fish swarm algorithm

PETROLEUM SCIENCE AND TECHNOLOGY 2024年

作者： Hallai, Said Dobbi, Abdelmadjid Kechiched, Rabah Univ Kasdi Merbah Dept Earth & Universe Sci Lab Underground Oil Gas & Aquifer Reservoirs Route GhardaiaBP 511 Ouargla 30000 Algeria

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the artificial fish swarm algorithm (AFSA) with the Back Propagation (BP) algorithm in an artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young's model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model's applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

关键词： Rate of penetration artificial fish swarm algorithm artificial neural network drilling particle swarm optimization, multi-layer perceptron

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A Grey wolf optimizer combined with artificial fish swarm algorithm for engineering design problems

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AIN SHAMS ENGINEERING JOURNAL 2024年第7期15卷

作者： Zhang, Hongzhi Zhang, Yong Niu, Yixing He, Kai Wang, Yukun Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

For the problems of Grey wolf optimizer (GWO) easy to fall into local optimum and lack of population diversity, this thesis raises a Grey wolf optimizer combined with an artificial fish swarm algorithm (AFGWO). First, the search method of grey wolves is improved by combining the clustering behavior of artificial fish swarm algorithm (AFSA) which avoids them falling into a local optimum. Second, to make the exploration and exploitation more balanced, the individual position of the worst grey wolf is updated by combining the foraging behavior of AFSA. Third, AFGWO adds quadratic interpolation and elite reverse learning to enrich population diversity. AFGWO is compared with other 10 algorithms on CEC2017 to evaluate its performance. The experimental results and four statistical analysis methods show that the proposed AFGWO based on above three improvement strategies has good solving ability and stability, and outperforms other comparative algorithms. In addition, AFGWO solves four classical engineering design problems well and demonstrates its good ability to solve realistic optimization problems. In general, AFGWO improved by the above strategy is better than GWO in performance, and is also very competitive with other intelligent algorithms.

关键词： Grey wolf optimizer artificial fish swarm algorithm Quadratic interpolation Elite reverse learning Engineering design problems

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Whale Optimization algorithm Based on artificial fish swarm algorithm 8th

Whale Optimization Algorithm Based on Artificial Fish Swarm ...

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8th International Conference on artificial Intelligence and Security (ICAIS)

作者： Bo, Xiong Feng Wenlong Zhang, Jin Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Towson Univ Dept Comp & Informat Sci 8000 York Rd Towson MD 21252 USA

ISBN: (纸本)9783031067884;9783031067877

The traditional whale algorithm has some shortcomings such as low solution accuracy and falling into local optimization easily. Aiming at these, a new whale optimization algorithm (NWOA) based on the artificial fish swarm algorithm is proposed, which combined with the advantages of artificial fish swarm algorithm that the crowding factor is used to limit the cluster size to promote the algorithm to be more optimized widely. The new whale optimization algorithm introduces the concept of crowding in the artificial fish swarm algorithm (AFSA) into the whale algorithm and changes characteristics of whale populations, and it overcomes the shortcoming of the traditional whale algorithm that individual whales perform different foraging behaviors according to the size of the random probability number. By calculating the degree of crowding around the prey, the foraging behavior of individual whales is designed to improve the convergence speed and optimization accuracy of the whale algorithm and enhance the stability of the algorithm. The simulation experiment is conducted by selecting 9 benchmark test functions. The experimental results show that the convergence speed and algorithm stability of the algorithm in this paper are significantly improved compared to the traditional whale algorithm.

关键词： Whale optimization algorithm artificial fish swarm algorithm algorithm optimization Random probability number Crowdedness

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Mechanism isomorphism identification based on artificial fish swarm algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第21期235卷 5421-5433页

作者： Yi, Huijun Wang, Jianpei Hu, Yongle Yang, Ping Univ Sci & Technol Hunan Prov Key Lab Intelligent Mfg Technol High p Changsha Peoples R China Nanjing Polytech Inst Nanjing Peoples R China Jiangsu Univ Sch Mech Engn Zhenjiang Jiangsu Peoples R China

The aim of this paper is to propose a practical solution for mechanism kinematic chain isomorphism identification - an artificial fish swarm algorithm. The artificial fish model of mechanism isomorphism identification is established, and behavioral way of the artificial fish is designed. According to isomorphism identification features of topological graph, the process of mechanism isomorphism identification based on artificial fish swarm algorithm is confirmed. The rationality and reliability of artificial fish swarm algorithm on the isomorphic identification of mechanism have been illustrated by a specific example, which provides a new method for intelligent CAD system design of mechanism. It builds a basis for future work in isomorphism identification of mechanism with high efficiency. Isomorphic identification of mechanism will contribute to rational qualitative analysis of mechanism design, perfection of irrationality can be done timely, which is the key factor for mechanical manufacturing. In this paper, we introduce the mechanism kinematic chain firstly, then optimization of artificial fish swarm algorithm is illustrated, and it is shown that how fish swarm algorithm is applied to mechanism kinematic chain. Finally, the feasibility and efficiency of the method are verified by the example of 10 bars, and the complex mechanism can be identified by the example of 14 bars and 18 bars.

关键词： Mechanism kinematic chain isomorphism identification artificial fish swarm algorithm simulation intelligent CAD

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Computation Scheduling of Multi-Access Edge Networks Based on the artificial fish swarm algorithm

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IEEE ACCESS 2021年 9卷 74674-74683页

作者： Li, Tiantian Yang, Feng Zhang, Depeng Zhai, Linbo Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

With the increasing requirements for computing in modern society, Multi-access Edge Computing (MEC) has received widespread attention for meeting low-latency. In MEC network, mobile devices can offload computing-intensive tasks to edge servers for computing. Wireless Power Transmission (WPT) provides initial energy for mobile devices, and the tasks of mobile devices consume energy when they are locally calculated or completely offloaded. The combination of the two technologies forms the Wireless Powered Mobile Edge Computing (WP-MEC) network. In this article, considering the impact of WPT transmission time tau(0), we study the offloading and scheduling of tasks for multiple mobile devices in the WP-MEC network, which is an NP-hard problem. We formulate this scheduling problem to minimize the time delay under the constraint of WPT transmission energy. We regard our problem studied in this paper as a multidimensional knapsack problem (MKP). The difference is that the knapsack capacity in MKP is limited, while in our problem, the knapsack that one item can choose is limited. Therefore, we improve the artificial fish swarm algorithm (AFSA) and propose Computation Scheduling Based on the artificial fish swarm algorithm (CS-AFSA) to find the optimal scheduling. We encode a scheduling scheme as an artificial fish and regard the delay corresponding to the scheduling as the optimization object. The optimal artificial fish can be gradually approached and determined through the swarm, follow and prey behavior of artificial fish. The optimal artificial fish is the optimal scheduling scheme. More importantly, based on the original behavior of AFSA, we also improve the scheme that does not meet the WPT energy constraint, including the modification of infeasible artificial fish and insufficient artificial fish. Besides, we also consider how to find the best WPT transmission time tau(0). Finally, we perform data simulation on the proposed algorithm.

关键词： artificial fish swarm algorithm WP-MEC network WPT transmission time

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Location Selection Strategy of Distribution Centers Based on artificial fish swarm algorithm Improved by Bacterial Colony Chemotaxis

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JOURNAL OF INTERNET TECHNOLOGY 2021年第3期22卷 685-695页

作者： Fei, Teng Zhang, Liyi Zhang, Xiaoqin Chen, Qi Liang, Jingyi Tianjin Univ Commerce Dept Informat Engn Tianjin Peoples R China

The distribution center (DC) is crucial in the connection between the suppliers and customers, thus the strategy for the selection and optimization of distribution center location plays an important role in logistics systems. In order to minimize the total cost, a distribution center location model with the lowest total cost is established. Accordingly, a distribution center location strategy based on artificial fish swarm algorithm improved by bacterial colony chemotaxis (BCC-AFSA) is proposed aiming to obtain a more feasible distribution center location strategy promptly. The algorithm of BCC-AFSA applies bacterial colony chemotaxis (BCC) to artificial fish swarm algorithm (AFSA) when the results do not change or change infinitesimally after multiple iterations. As result, the global optimization ability of the algorithm is improved. Through algorithm tests and example simulation, it is shown that the improved BCC-AFSA is more effective in the location selection strategy of distribution centers.

关键词： Distribution center Bacterial colony chemotaxis artificial fish swarm algorithm Location Strategy

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Research on automatic driving strategy optimization method of high-speed train based on artificial fish swarm algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2024年第3期16卷

作者： Bai, Shuai Qiao, Yaqiong Liang, Zhiguo Liu, Qingmeng Liu, Yiming China Acad Railway Sci Co Ltd Signal & Commun Res Inst Beijing Peoples R China Lanzhou Jiaotong Univ Lanzhou Peoples R China China Acad Railway Sci Co Ltd Sci & Technol Informat Res Inst Beijing Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730000 Peoples R China

The existing automatic driving technology of high-speed railway trains has been able to complete the automatic driving of trains in normal scenes, and it has begun to develop in the direction of improving operational efficiency and optimizing driving strategies. Based on the current automatic driving technology, this paper proposes an optimization method of automatic driving strategy for high-speed trains based on an artificial fish swarm algorithm, aiming at reducing train energy consumption, improving train parking accuracy, train punctuality, passenger comfort, and train speed protection. Firstly, based on train dynamics, the dynamic parameter model of the train in the running process is described. Through the train dynamic parameter model, combined with line information such as speed limit, slope, and phase separation area, an artificial fish swarm algorithm is designed that aims at safety, punctuality, energy saving, comfort, and parking accuracy. The artificial fish swarm algorithm is used to simulate the optimal driving strategy of train running and generate the train driving curve. Finally, the driving curve simulated by the artificial fish swarm algorithm is compared with the driving curve simulated by the genetic algorithm and the actual driving curve of the Beijing-Shenyang high-speed train, which shows that the proposed method is effective and the convergence speed of the artificial fish swarm algorithm is better than that of the genetic algorithm.

关键词： artificial fish swarm algorithm driving strategy dynamic modeling high-speed train simulation and modeling

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The artificial fish swarm algorithm Optimized by RNA Computing

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2021年第4期55卷 346-357页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Liang, Jingyi Tianjin Univ Commerce Informat Engn Coll Tianjin 300134 Peoples R China Tianjin Univ Commerce Econ Coll Tianjin 300134 Peoples R China

In the initial period, the peculiarity of artificial fish swarm algorithm is of fast searching speed and high optimization accuracy, but in the later period, the convergence speed is always slow, and artificial fish tend to gather around the local optimum. Therefore, the solving ability of the algorithm becomes weak and the global optimal value is hard to obtain. Considering the introduction of RNA computation based on biomolecular operations, the optimization capability of traditional algorithm can be enhanced effectively. Therefore, RNA computing is introduced to artificial fish swarm algorithm, and a modified artificial fish swarm algorithm is presented on the grounds of RNA computing. In the later period of artificial fish swarm algorithm, the transformation, replacement and recombination operations in RNA computation are applied to increase diversity of artificial fish, so as to further the convergence speed and optimization capability of the algorithm. In the meantime, the improved algorithm, RNA-AFSA, is tested by four typical functions, and the results prove that the modified artificial fish swarm algorithm has better optimization effects in search accuracy, stability, and other aspects.

关键词： artificial fish swarm algorithm RNA computation RNA-AFSA function optimization

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Severe Convection Weather Identification Model Based on Rough Set Theory and artificial fish swarm algorithm 40

Severe Convection Weather Identification Model Based on Roug...

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40th Chinese Control Conference (CCC)

作者： Guo, Jianlin Lu, Zhiying Wang, Chenyi Wu, Haopeng Ding, Xudong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9789881563804

In order to solve the problem of identifying severe convection weather, this paper proposes a model of identifying severe convection weather based on rough set theory and artificial fish swarm algorithm. First, based on the radar reflectivity images, the echo attributes of the severe convection area are analyzed and extracted. Second, this paper proposes a discretization method based on rough set theory. This method discretizes the meteorological data and establishes a decision table. Third, the improved artificial fish swarm algorithm is used to reduce attributes and obtain the minimum attribute set. Finally, the classification and regression tree (CART) algorithm and the cost-complexity pruning (CCP) principle are used to establish an objective model for identifying severe convection weather. Experimental results show that the model in this paper can recognize severe convection weather under the condition of a few attributes and the identification model has high accuracy.

关键词： Severe convection rough set attribute reduction artificial fish swarm algorithm classification and regression tree

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An Innovative Approach To Software Modularization Based On The artificial fish swarm algorithm 22

An Innovative Approach To Software Modularization Based On T...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

作者： Jianqiang Pan Cheng Zhang Huihui Jia Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology State Key Lab for Novel Software Technology Anhui University China

ISBN: (纸本)9781450397148

It gets more and more expensive to maintain the complete software system as time goes on since the software architecture grows more complicated and the software code is more difficult to understand. This issue may be solved by taking the essential parts out of the source code and organizing them into the appropriate subsystems. Hierarchy-based partitioning is more complex and less successful in solving issues with huge software modules. In order to do this, this work suggests an innovative artificial fish swarm method as a meta-heuristic to solve the software module clustering problems (SMCPs) and presents a mutation operator and a local search strategy to handle the premature situation. The performance of the suggested method is assessed on a range of real-world software systems by comparison to the most recent meta-heuristic methods. The outcomes of the experiments demonstrate that the suggested methodology works better than those of other methods.

关键词： Software refactoring artificial fish swarm algorithm Software module clustering

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