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39th Chinese Control Conference (CCC)

作者： Qi, Yongshuang Zhi, Pengfei Ye, Hui Zhu, Wanlu Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China Jiangsu Zhenjiang Shipyard Grp Co LTD Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9789881563903

New energy is an important direction for the development of unmanned ships. The intermittent and random characteristics of new energy have seriously affected the support of new energy to the load. The access of energy storage devices effectively solves the instability of new energy. This article uses the three lithium battery power supply system as an example to explore the relationship between the service life of the unmanned ship's lithium battery pack power supply system and the battery capacity, charging current, and discharge current. At the same time, the artificial fish swarm algorithm is used to optimize the battery capacity, charging current, and discharge current of the three-lithium battery-powered system to extend the life of the three-lithium battery-powered system.

关键词： Unmanned Ship lithium battery power supply system three lithium battery power supply system artificial fish swarm algorithm service life new energy

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Inversion of particle size distribution using the artificial fish swarm algorithm

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TM-TECHNISCHES MESSEN 2019年第1期86卷 34-44页

作者： Shan, Liang Xu, Liang Cao, Lixia Hong, Bo Wang, Daodang Guo, Tiantai Kong, Ming China Jiliang Univ Coll Metrol Technol & Engn Hangzhou 310018 Zhejiang Peoples R China China Jiliang Univ Coll Informat Engn Key Lab Electromagnet Wave Informat Technol & Met Hangzhou 310018 Zhejiang Peoples R China

In the small-angle forward scattering technique, an inversion method of particle size distribution (PSD) using artificial fish swarm algorithm (AFSA) is proposed, which can acquire optimal characteristic parameters of the PSD. Simulations are performed to verify the effectiveness of AFSA, in which spheroidal particles of a unimodal distribution that conforms to Johnson's S-B function is inversed at different levels of gaussian white noise. The comparison of the smooth objective function and non-smooth objective function of AFSA is conducted and discussed. The measurement system of the small-angle forward scattering based on Mie scattering theory is constructed and experiments are performed to verify the practicability of AFSA. A CCD sensor is adopted to receive the scattered light instead of the conventional photodetector. Pinhole is first proposed to replace standard particles and calibrate the measurement system, which can eliminate the influence of particle concentration. After the calibration experiments, the PSD of standardized polystyrene microspheres are measured and modified. Both the simulation and experiment results indicate that the PSD can be successfully inverted by AFSA with high reliability and stability in certain conditions, and the system calibration can further improve the inversion accuracy.

关键词： artificial fish swarm algorithm particle size distribution Mie scattering theory pinhole calibration

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Optimal chiller loading by improved artificial fish swarm algorithm for energy saving

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MATHEMATICS AND COMPUTERS IN SIMULATION 2019年 155卷 227-243页

作者： Zheng, Zhi-Xin Li, Jun-qing Duan, Pei-yong Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Southeast Univ Minist Educ China Key Lab Comp Network & Informat Integrat Nanjing 211189 Jiangsu Peoples R China

This study presents an improved artificial fish swarm algorithm (VAFSA) to solve the optimal chiller loading (OCL) problem, using minimal power consumption of chillers and cooling towers as the objective function. In the proposed algorithm, several components are developed, such as initialization method based decimal system, food concentration function, bulletin board approach, target position search mechanism, and position move method. Then, the adjustment strategy of search range of artificial fish, which combines the global search with local search, is proposed for improving the search ability of VAFSA. To testify the performance of VAFSA, three well-known case studies are tested with the comparison with other recently reported approaches. The experimental results show that VAFSA can obtain power saving compared with other approaches, and also with the competitive convergence ability. The proposed algorithm can be used as an attractive alternative method to operate air-conditioning systems. (C) 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Optimal chiller loading Energy saving artificial fish swarm algorithm

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Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2019年 83卷 425-436页

作者： Zhuang, D. Y. Ma, K. Tang, C. A. Liang, Z. Z. Wang, K. K. Wang, Z. W. Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Inst Rock Instabil & Seism Res Dalian 116024 Liaoning Peoples R China China Coal Res Inst Mine Safety Technol Branch Beijing 100013 Peoples R China

Fast and efficient determination of the mechanical parameters of surrounding rock masses is vitally important to the calculation and evaluation of the stability of surrounding rock masses in tunnel engineering. In this paper, a displacement back-analysis (DBA) model is proposed to identify the mechanical parameters based on support vector regression (SVR) optimized by multi-strategy artificial fish swarm algorithm (MAFSA). The MAFSA adopts the differential evolution strategy, the particle swarm optimization strategy, the adaptive step size and phased vision strategy on the basis of artificial fish swarm algorithm (AFSA) to enhance the global search capability and improve convergence speed and optimization accuracy. Then, the kernel width and the penalty parameter of SVR are optimized by MAFSA, forming into MAFSA-SVR. Meanwhile, the training and testing samples for MAFSA-SVR are constructed by orthogonal design and forward calculation by FLAC(3D) code. Finally, the DBA model is established based on MAFSA-SVR and applied to the mechanical parameter inversion of surrounding rock masses in the Heshi tunnel with the following conclusion: the relative errors of all the mechanical parameters are less than 8% between the inversed values of the DBA model based on MAFSA-SVR and the actual values. The method proposed in this paper could provide an efficient tool for the mechanical parameter inversion of the tunnel surrounding rock masses.

关键词： Displacement back-analysis Support vector regression artificial fish swarm algorithm Orthogonal test Mechanical parameter inversion

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

Severe Convection Weather Identification Model Based on Roug...

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第40届中国控制会议

作者： Jianlin Guo Zhiying Lu Chenyi Wang Haopeng Wu Xudong Ding School of Electrical and Information Engineering Tianjin University

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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Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2073-2083页

作者： Ma, Changxi He, Ruichun Lanzhou Jiaotong Univ Sch Traff & Transportat Anning West Rd Lanzhou 730070 Gansu Peoples R China

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system.

关键词： Adaptive genetic algorithm artificial fish swarm algorithm Green wave traffic Control system Optimization

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

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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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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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Guidance Systematic Error Separation for Mobile Launch Vehicles Using artificial fish swarm algorithm

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IEEE ACCESS 2019年 7卷 31422-31434页

作者： Zhou, Xuanying Wang, Zhengming Li, Dong Zhou, Haiyin Qin, Yongrui Wang, Jiongqi Natl Univ Def Technol Coll Liberal Arts & Sci Changsha 410073 Hunan Peoples R China PLA 91550 Unit 94 Dalian 116023 Peoples R China Univ Huddersfield Elect Sch Comp & Engn Huddersfield HD1 3DH W Yorkshire England

For missile's accuracy assessment, an accurate separation about the guidance of systematic errors is a critical part. Based on the vehicles from a mobile launcher platform, this paper proposes a nonlinear error separation model and a corresponding method in consideration of the ill-conditioning of the environmental function matrix, and the coupling of the guidance instrumental errors and the initial errors. The nonlinear model is built in combination with the tracking data. For the error separation problem with ill-conditioning, the traditional nonlinear methods can only slightly weaken the degree of ill-conditioning rather than solve it. To address this issue, this paper puts forward a novel guidance systematic error separation method based on the artificial fish swarm algorithm (AFSA). We first provide a brief introduction to AFSA and then analyze the convergence and the optimality of parameter estimation. Furthermore, we present the details of our novel algorithm that can address the guidance systematic error separation problem. We conduct a set of simulations to verify our approach. The simulation results confirm that our approach, which is based on AFSA, can improve the error separation accuracy effectively and perform better than the Bayesian estimation based on the traditional linear model and the Bayesian maximum a posteriori estimation based on the nonlinear model.

关键词： Aircraft guidance system error separation nonlinear combination model artificial fish swarm algorithm separation performance

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Improved discrete artificial fish swarm algorithm combined with margin distance minimization for ensemble pruning

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 32-46页

作者： Zhu, Xuhui Ni, Zhiwei Ni, Liping Jin, Feifei Cheng, Meiying Li, Jingming Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Anhui Peoples R China Huzhou Univ Business Sch Huzhou 313000 Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Peoples R China

Ensemble pruning aims to achieve a good result in classification using a smaller size of classifiers by finding the optimal sub-ensemble. Diversity and accuracy of classifiers are widely recognized to be two key factors for a successful ensemble. There is a tradeoff between the diversity and the accuracy of classifiers, which makes the ensemble perform the best. Existing ensemble pruning approaches always find the tradeoff using diversity measures or heuristic algorithms separately. Those pruning approaches based on diversity measures cannot exactly find the tradeoff;Those algorithms based on heuristic algorithms are not also to exhaustively search for it extracted from an initial pool of classifiers with a medium-scale or large-scale size. To address the issue, Improved Discrete artificial fish swarm algorithm combined with Margin distance minimization for Ensemble Pruning (IDAFMEP) is proposed using a combination of diversity measure and heuristic algorithm. First, the classifiers in a constructed initial pool are pre-pruned using Margin Distance Minimization (MDM), which can downsize the classifiers who perform badly, and markedly alleviate the computational complexity of ensemble pruning. Second, the final ensemble is efficiently achieved from the retaining classifiers after pre-pruning based on MDM using the proposed Improved discrete artificial fish swarm algorithm (IDAFSA). Experimental results on 29 datasets from the UCI Machine Learning Repository demonstrate that IDAFMEP can achieve better results than the original ensemble and other state-of-the-art pruning approaches, and that its validity and effectiveness. It provides a new research idea for ensemble pruning.

关键词： artificial fish swarm algorithm Margin distance minimization Ensemble pruning

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