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

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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Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

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KNOWLEDGE-BASED SYSTEMS 2011年第3期24卷 378-385页

作者： Shen, Wei Guo, Xiaopen Wu, Chao Wu, Desheng Univ Toronto RiskLab Toronto ON M5S 3G3 Canada N China Elect Power Univ Sch Business & Adm Beijing 102206 Peoples R China Univ Waterloo Waterloo ON N2L 3G1 Canada Reykjavik Univ Menntavegur 1 IS-101 Reykjavik Iceland

Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external factors, they pose a great challenge to researchers who try to predict them. In this paper, we select a radial basis function neural network (RBFNN) to train data and forecast the stock indices of the Shanghai Stock Exchange. We introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a K-means clustering algorithm is optimized by AFSA in the learning process of RBF. To verify the usefulness of our algorithm, we compared the forecasting results of RBF optimized by AFSA, genetic algorithms (GA) and particle swarm optimization (PSO), as well as forecasting results of ARIMA. BP and support vector machine (SVM). Our experiment indicates that RBF optimized by AFSA is an easy-to-use algorithm with considerable accuracy. Of all the combinations we tried in this paper, BIAS6 + MA5 + ASY4 was the optimum group with the least errors. (C) 2010 Elsevier B.V. All rights reserved.

关键词： artificial fish swarm algorithm Radial basis function neural network K-means clustering algorithm Data mining Shanghai Stock Exchange Index

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Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast

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APPLIED INTELLIGENCE 2018年第7期48卷 1757-1775页

作者： Zhu, Xuhui Ni, Zhiwei Cheng, Meiying Jin, Feifei Li, Jingming Weckman, Gary 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 Ohio Univ Dept Ind & Syst Engn Athens OH 45701 USA

Urban haze pollution is becoming increasingly serious, which is considered very harmful for humans by World Health Organization (WHO). Haze forecasts can be used to protect human health. In this paper, a Selective ENsemble based on an Extreme Learning Machine (ELM) and Improved Discrete artificial fish swarm algorithm (IDAFSEN) is proposed, which overcomes the drawback that a single ELM is unstable in terms of its classification. First, the initial pool of base ELMs is generated by using bootstrap sampling, which is then pre-pruned by calculating the pair-wise diversity measure of each base ELM. Second, partial-based ELMs among the initial pool after pre-pruning with higher precision and with greater diversity are selected by using an Improved Discrete artificial fish swarm algorithm (IDAFSA). Finally, the selected base ELMs are integrated through majority voting. The Experimental results on 16 datasets from the UCI Machine Learning Repository demonstrate that IDAFSEN can achieve better classification accuracy than other previously reported methods. After a performance evaluation of the proposed approach, this paper looks at how this can be used in haze forecasting in China to protect human health.

关键词： Selective ensemble artificial fish swarm algorithm Extreme learning machine Haze forecast

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Diagnostic Complexity of Regional Groundwater Resources System Based on time series fractal dimension and artificial fish swarm algorithm

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WATER RESOURCES MANAGEMENT 2013年第7期27卷 1897-1911页

作者： Yu, Miao Liu, Dong Bazimenyera, Jean de Dieu Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Water Saving Agr Univ Heilongjiang Prov Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Minist Agr Key Lab High Efficient Utilizat Agr Water Resourc Harbin 150030 Heilongjiang Peoples R China

Due to the increasing high rate of economic development and population, groundwater resources of Jiansanjiang in China are affected by both anthropogenic and natural factors. In order to achieve optimal local allocation of water resources and promotion of local economic development, a suitable method for measuring the complexity of groundwater resources system is very important. In this paper, time series fractal dimension based on the curve length calculation combined with the artificial fish algorithm for the intelligent efficient fitting of data were applied to diagnose groundwater sequence in Jiansanjiang. Fractal dimension values of monthly groundwater depth series in 15 districts of Jiansanjiang Branch Bureau and their average complexity were calculated and the results revealed that the complexity of north district is the highest while that of southern district is the lowest. Our analysis also revealed that the most important influencing factor of local groundwater depth dynamics variation is the human activities and results confirmed that combined fractal theory and artificial fish algorithm for extraction hydrological time series complexity feature is feasible and can be applied in studying regional hydrological process. It also provided a scientific basis for achieving sustainable utilization of the regional groundwater resource.

关键词： Time series fractal dimension artificial fish swarm algorithm Ground water depth Complexity Jiansanjiang Branch Bureau

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Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition

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APPLIED INTELLIGENCE 2020年第8期50卷 2312-2327页

作者： Gao, Yikai Xie, Linbo Zhang, Zhengdao Fan, Qigao Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

In this paper, a twin support vector machine (TWSVM) based on improved artificial fish swarm algorithm (IAFSA) for fire flame recognition is proposed in view of the large computation burden and slow classification speed of the traditional support vector machine (SVM). Twin support vector machine is a machine learning algorithm developing from standard support vector machine. However, twin support vector machine cannot deal with the parameter selection problem well. The difficulty of parameter selection may greatly restrict the application of TWSVM in flame recognition problem. So a novel artificial fish swarm algorithm (AFSA) is used to solve the parameter selection problem of TWSVM. In order to make up for the drawbacks of the basic AFSA, the chaotic transformation is first applied to initialize the position of artificial fish swarm since it may be non-uniformly initialized in the basic artificial fish swarm algorithm. Then, the Cauchy mutation is used to make the fish swarm jump out of the local optimal solution after continuously expanding the visual scope of the artificial fish during the foraging procedure. An adaptively step-size adjusting method is then developed to optimize the moving steps of the swarming and following behaviors in order to accelerate the convergence speed of the developed algorithm. Last, to further improve the efficiency and accuracy of the algorithm, an elimination and regeneration mechanism based on adaptive t-distribution mutation is utilized to update the artificial fish swarm at each iterative procedure. Experimental results show that the TWSVM algorithm based on improved artificial fish swarm algorithm is a more effective method to identify the flame and greatly improves the accuracy and real-time performance of the flame recognition compared with PSO-TWSVM, Grid-TWSVM, GA-TWSVM, FOA-TWSVM, GSO-TWSVM, AFSA-TWSVM and the traditional SVM.

关键词： Twin support vector machine artificial fish swarm algorithm Fire flame recognition Color model

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Research on coverage holes repair in wireless sensor networks based on an improved artificial fish swarm algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2022年第4期15卷 312-330页

作者： Dongliang, Li Jiaozuo Univ Fac Informat Engn Jiaozuo 454000 Peoples R China

In a wireless sensor network (WSN), holes are formed when nodes become invalid. To resolve this problem, holes repairing algorithm based on fish swarm optimisation in wireless sensor network (HRFSO) is proposed in this paper. In the algorithm, network coverage is served as the objective function, and the biological behaviours of artificial fish are used to simulate node movements. The new actions of jumping and survival of the fittest are defined besides foraging, rear-ending and grouping to improve the convergence of optimisation. Self-adaptive vision and step length are used when updating the status of artificial fish. Failed holes are repaired by moving the sensor node with the shortest distance. The simulation results show that the algorithm is suitable for repairing holes with fast speed by moving fewer nodes. It can increase WSN coverage with better repairing results, faster convergence, higher accuracy, efficiency, and robustness. The results also show the lifetime of the network can be prolonged.

关键词： network coverage AFSA artificial fish swarm algorithm WSN wireless sensor network hybrid network robustness

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Multi-unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2024年第4期72卷

作者： Ding, Tao Zhong, Wenhan Cai, Yufeng China Jiliang Univ Hangzhou Peoples R China Zhejiang Light Ind Prod Inspect & Res Inst Hangzhou Peoples R China

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

关键词： odor source location artificial fish swarm algorithm unmanned aerial vehicle active olfaction

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Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm

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SOFT COMPUTING 2014年第3期18卷 457-468页

作者： Wang, Jie Sheng Han, Shuang Guo, Qiu Ping Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

Conversion rate in the Polyvinylchloride (PVC) polymerizing process has a certain influence on the molecular weight of PVC, porosity, absorption rate of plasticizer, vinyl chloride monomer (VCM) residue and thermal stability. Therefore, a predictive model based on echo state networks (ESN) method optimized by the artificial fish swarm algorithm (AFSA) is proposed to predict the conversion velocity. Firstly, the hot balancing mechanisms of polymerizer and the influenced factors of convention rate of VCM are analyzed in details. Then the auxiliary variables of the predictive model kernel are selected by using the kernel principal component analysis method for reducing the model dimensionality. Thirdly, the structure parameters of the ESN are optimized by the AFSA to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. The artificial fish swarm behaviors, such as foraging, swarming, chasing, random, are introduced in details. Finally, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical index and satisfy the real-time control requirements of PVC polymerizing production process.

关键词： Polymerize process Echo state network Kernel principal component analysis artificial fish swarm algorithm

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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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An adaptive simulated annealing and artificial fish swarm algorithm for the optimization of multi-depot express delivery vehicle routing

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INTELLIGENT DATA ANALYSIS 2022年第1期26卷 239-256页

作者： Yuan, Mengfei Kan, Xiu Chi, Chihung Cao, Le Shu, Huisheng Fan, Yixuan Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Southeast Univ Sch Math Nanjing Jiangsu Peoples R China Data61 CSIRO Hobart Tas Australia Donghua Univ Sch Sci Shanghai Peoples R China

In this paper, the Capacitated Vehicle Routing Problem (CVRP) of multi-depot express delivery is investigated based on the actual express delivery business in Beijing and driving intention-based road network. An Adaptive Simulated Annealing and artificial fish swarm algorithm (A-SAAFSA) is proposed to solve the CVRP. The basic ideas are use a "certainty" probability to accept the worst solution through the Metropolis criterion in the search process, and a strategy of adjusting the swimming direction to avoid falling into the local optimal solution. Moreover, an adaptive visual strategy, which adjusts the visual range adaptively in real time according to the current solution quality, is used to ensure the efficient searching and accuracy of the algorithm. Experimental results show that the A-SAAFSA algorithm outperforms four well-known algorithms, namely simulated annealing and artificial fish swarm algorithm, artificial fish swarm algorithm, simulated annealing algorithm, and genetic algorithm.

关键词： Express delivery CVRP road network simplification artificial fish swarm algorithm simulated annealing algorithm adaptive visual strategy

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