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Spread binary artificial fish swarm algorithm combined with double-fault measure for ensemble pruning

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期36卷 4375-4387页

作者： Zhu, Xuhui Ni, Zhiwei Ni, Liping Jin, Feifei Cheng, Meiying Li, Jingming Hefei Univ Technol Sch Management Hefei Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Anhui Peoples R China Huzhou Univ Business Sch Huzhou Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu Peoples R China

Ensemble pruning is usually used to improve classification ability of an ensemble using less number of classifiers, and it is an NP-hard problem. Existing ensemble pruning approaches always find the optimal sub-ensemble using diversity of classifiers or running heuristic search algorithms separately. Diversity and accuracy of classifiers are widely recognized as two important properties of an ensemble. The increase of the diversity of classifiers must lead to the decrease of the average accuracy of the whole classifiers, and vice versa, so there is a tradeoff between diversity and accuracy of classifiers. Finding the tradeoff is the key to a successful ensemble. Heuristic algorithms have good results when it comes to finding the tradeoff, but it is unfeasible to do an exhaustive search. Hence, we propose a Spread Binary artificial fish swarm algorithm combined with a Double-fault measure for Ensemble Pruning (SBAFDEP) using a combination of diversity measures and heuristic algorithms. First, the classifiers in an initial pool are pre-pruned using a double-fault measure, which significantly alleviates the computational complexity of ensemble pruning. Second, the final ensemble is efficiently assembled from the retaining classifiers after pre-pruning using the proposed Spread Binary artificial fish swarm algorithm (SBAFSA). Simulation and experiment results on 25 UCI datasets show that SBAFDEP performs better than other state-of-the-art pruning approaches. It provides a novel research idea for ensemble pruning.

关键词： artificial fish swarm algorithm spread behavior double-fault measure diversity ensemble pruning

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Identification of thermal conductivity of transient heat transfer systems based on an improved artificial fish swarm algorithm

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第14期148卷 6969-6987页

作者： Wang, Shoubin Li, Yunlong Zhou, Yuan Peng, Guili Xu, Wenbin Tianjin ChengJian Univ Sch Control & Mech Tianjin 300384 Peoples R China Harbin Inst Technol Sch Instrument Sci & Engn Harbin 150001 Peoples R China

Thermal conductivity is employed as a measure of the strength of heat transfer from a substance. There is a lack of accurate methods for solving thermal conductivity in heat transfer inverse problems due to the ill-posedness and non-linearity of the problem. In this paper, an intelligent solution algorithm based on the artificial fish swarm algorithm is proposed and a proportional control algorithm is introduced to invert the heat transfer coefficient of a transient heat conduction system. In the forward heat conduction process, a finite difference algorithm is utilized to solve for the required measurement point temperature of the heat conduction system. In the inverse problem solution, the performance of the algorithm is compared before and after its improvement, and the inversion is compared in the rectangular and circular heat transfer domains under the influence of different heat transfer materials, interference sources and errors. The results show that in the absence of disturbances, the inversion errors of the improved artificial fish swarm algorithm in the rectangular and circular domains are 0.4% and 0.1%, respectively. In the presence of large measurement errors (sigma = 0.5 ), the inversion errors in the rectangular and circular domains are 8.7% and 2.4%, respectively, which validate the accuracy of the algorithm.

关键词： Heat transfer inverse problem artificial fish swarm algorithm Proportional regulation Thermal conductivity Finite difference method

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Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and artificial fish swarm algorithm

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IEEE ACCESS 2020年 8卷 42864-42876页

作者： Feng, Yingying Zhao, Shasha Liu, Hui Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China

There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research.

关键词： Feedback K-means clustering artificial fish swarm algorithm network coverage algorithm optimization

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Research and Implementation of Parallel artificial fish swarm algorithm Based on Ternary Optical Computer

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MOBILE NETWORKS & APPLICATIONS 2022年第4期27卷 1397-1407页

作者： Li, Shuang Li, Wenjing Wang, Zhehe An, Dongdong Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 201418 Peoples R China Nanning Normal Univ Sch Logist Management & Engn Nanning 530299 Guangxi Peoples R China Hainan Trop Ocean Univ Coll Comp Sci & Technol Sanya 572022 Peoples R China

artificial fish swarm algorithm (AFS) is used in the field of function optimization problems widely. The traditional AFS algorithm has some problems such as long time to find the optimal solution and easy to fall into local optimality at the later stage of the search. We investigate design solutions and methods to implement parallel AFS algorithms by taking advantage of the large number of TOC processor bits and the easy scalability of processor bits. Firstly, we find out the parallel part of the algorithm by analyzing the traditional AFS algorithm and carry out the parallel design. Then we performed a detailed design of the algorithm implementation flow and analyzed the clock cycle. Finally, the correctness of our proposed parallel algorithm is verified on SD11. Compared with the serial AFS and parallel AFS algorithms based on electronic computers, the TOC-based AFS algorithm (TOC-PAFS) proposed in this paper effectively reduces the search time and improves the search performance of complex multi-peaked function optimization problems.

关键词： Ternary optical computer Ternary optical processor artificial fish swarm algorithm Parallel computing

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Path planning for autonomous surface vessels based on improved artificial fish swarm algorithm: a further study

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SHIPS AND OFFSHORE STRUCTURES 2023年第9期18卷 1325-1337页

作者： Zhao, Liang Bai, Yong Wang, Fang Bai, Jie Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou Peoples R China Harbin Engn Univ Sci & Technol Underwater Vehicle Technol Lab Harbin Peoples R China

Featuring agile controllability, high-level autonomy, and powerful field operational advantages, autonomous surface vessels (ASVs) are gaining increasing attention worldwide. Central to the control of ASVs, path planning is one of the key technologies that ensure the navigation safety. However, many of the previous works to solve the path planning problem are to find a collision-free and shortest path, but the solutions are not satisfied by the safety requirements and the non-holonomic constraints of ASVs. This paper expands on our previous work on the improved artificial fish algorithm (IAFSA) to address these challenges. The expanding technique is introduced to modify the grid cost, and a B-spline-based path smoother is applied to the algorithm to enhance its feasibility in cooperating with the ASV controller. Simulation experiments have been conducted to illustrate the effectiveness of the proposed method. Moreover, the new algorithm is tested in the USV control system model in a practical environment. The simulation results have demonstrated that the proposed method is more efficient than the other state-of-the-art algorithms, and the simulation with the ASV model illustrated its excellent performance cooperating with the control system. Therefore, the proposed method can be considered as a reliable algorithm to solve the path planning problem of ASVs.

关键词： Path planning artificial fish swarm algorithm unmanned surface vehicle Optimization Collision avoidance USV

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Condition assessment of a concrete filled steel tube arch bridge using in-situ vibration measurements and an Improved artificial fish swarm algorithm

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COMPUTERS & STRUCTURES 2024年 291卷

作者： Qin, Shiqiang Feng, Jiacheng Tang, Jian Huo, Xuejin Zhou, Yunlai Yang, Fei Wahab, Magd Abdel Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Hubei Peoples R China China Railway Major Bridge Reconnaissance & Design Wuhan 430074 Hubei Peoples R China Xi An Jiao Tong Univ Sch Aerosp Engn State Key Lab Strength & Vibrat Mech Struct Xian 710049 Peoples R China Infrastructure Inspection Res Inst China Acad Railway Sci Corp Ltd Beijing 100081 Peoples R China Univ Ghent Fac Engn & Architecture Soete Lab Technologiepark Zwijnaarde 903 B-9052 Zwijnaarde Belgium Yuan Ze Univ Coll Engn Taoyuan City Taiwan

This study aims to assess the condition of a Concrete-Filled Steel Tube (CFST) arch bridge using in-situ vibration measurements, Finite Element Model Updating (FEMU) and an Improved artificial fish swarm algorithm (IAFSA). Dynamic coefficients, derailment coefficients and wheel unloading rates are extracted from the responses of running safety assessment. An updated finite element model using a kriging model and IAFSA is employed to evaluate the strength and rigidity of the structures. The results indicate that the natural frequencies and damping ratios of the CFST arch bridge meet the requirements of design code. The maximum bridge dynamic coefficient is found to be 1.085, indicating that the bridge dynamic performance is good. The wheel unloading rates of vehicle and locomotive exceed the specification limit, indicating that the track condition has to be improved before the bridge is operated. By updating an initial FE model, the errors between the predicted and the measured parameters are reduced to less than 5%.

关键词： Continuous rigid frame Concrete filled steel tube Arch bridge Finite Element Model Updating artificial fish swarm algorithm

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An early warning model for the stuck-in medical drilling process based on the artificial fish swarm algorithm and SVM

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DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 779-796页

作者： Xian, Zhongyan Yang, Hai Southwest Petr Univ Sch Mech Engn Chengdu 610500 Peoples R China

To avoid the considerable challenges and losses caused by stuck drilling to normal drilling operations, this article analyses the mechanism of stuck drilling, then combines the artificial fish swarm algorithm (AFSA) and support vector machine (SVM), and finally proposes an early warning model for the stuck-in medical drilling process based on the AFSA and SVM. The model realizes real-time sticking risk warnings by using the four parameters of riser pressure, torque, speed and hook load collected in real time and promotes real-time drilling parameter monitoring for the real-time dynamic warning of sticking risk. By comparing the AFSA-SVM sticking prediction model with the particle swarm optimization model and the traditional cross-validation optimization model, it is found that the AFSA-SVM stuck prediction model accuracy can reach 97.561% and that the training and testing times are 5.874 s and 0.76 s, respectively. Its accuracy and computational efficiency are higher than those of the particle swarm optimization model and traditional cross-validation optimization model. In comparison with the existing technology, the four-parameter early sticking warning model based on AFSA-SVM presented in this paper shows powerful comprehensive performance and field application value.

关键词： Borehole cleaning Stuck drilling Machine learning artificial fish swarm algorithm Vector machine

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Application of the artificial fish swarm algorithm to Well Trajectory Optimization

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CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS 2019年第2期55卷 213-218页

作者： Sun, Tengfei Zhang, Hui Gao, Deli Liu, Shujie Cao, Yanfeng Beijing Univ Chem Technol Beijing 100029 Peoples R China CNOOC Res Inst Beijing Peoples R China China Univ Petr Dept Petr Engn Beijing Peoples R China

Drilling applications involve a number of global optimization problems that require finding the best extremum value of a nonlinear function of many variables. One of such problems is the choice of the optimal well drilling trajectory. Various trajectory optimization algorithms have been previously proposed, but they all suffer from some shortcomings. In the present paper, the shortest well length is used as the objective function, and optimization is performed by the artificial fish swarm algorithm (AFSA). The calculations have been carried out in the Matlab environment. Comparison of our calculations with previously published data suggests that AFSA optimization produces the best numerical results and the shortest trajectory, while in addition ensuring high stability and reliability. The algorithm has a simple structure and fast convergence, quickly producing a global optimum. AFSA thus may be used to calculate the optimal drilling trajectory.

关键词： artificial fish swarm algorithm well length drilling trajectory optimization

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Study on application of local neighborhood artificial fish swarm algorithm for 40 Gb/s adaptive second-order polarization mode dispersion compensation

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OPTICAL AND QUANTUM ELECTRONICS 2012年第15期44卷 749-758页

作者： Xu, Heng-Ying Bai, Cheng-Lin Key Lab Opt Commun Sci & Technol Shandong Prov Liaocheng 252000 Peoples R China Liaocheng Univ Phys Sci & Informat Engn Coll Liaocheng 252000 Peoples R China

Based on a newly improved artificial fish swarm algorithm- local neighborhood artificial fish swarm algorithm (LNAFSA), we studied its application for adaptive polarization mode dispersion (PMD) compensation. In the adopted local neighborhood structure, each artificial fish only communicated with other five neighboring artificial fishes. Under a simulated 40 Gb/s adaptive second-order PMD compensation for non-return- to-zero and carrier-suppressed return-to-zero formats, the study demonstrated the effectiveness of LNAFSA. In a defined searching process, the eye diagrams showed that the algorithm resulted in an effective compensation effect and produced the maximum consuming time within 300 ms. Much better performance than simplex algorithm and genetic algorithm was also observed. In tracking process, LNAFSA achieved the goal of compensation in about 14 ms.

关键词： Adaptive compensation Polarization mode dispersion artificial fish swarm algorithm Local neighborhood

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A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 86卷 165-176页

作者： Zhang, Zeqiang Wang, Kaipu Zhu, Lixia Wang, Yi Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China Auburn Univ Dept Math & Comp Sci Montgomery AL USA

To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper. First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs). Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem. The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors. An order crossover operator of the traditional genetic algorithm is employed in the prey stage. The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions. The proposed model after the defuzzification is validated by the LINGO solver. And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems. Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Disassembly line balancing problem Triangular fuzzy number Multi-objective optimization artificial fish swarm algorithm Pareto optimal solutions

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