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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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Medium and long-term wind power prediction based on artificial fish swarm algorithm combined with extreme learning machine

International Core Journal of Engineering

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International Core Journal of Engineering 2019年第10期5卷 265-272页

作者： Xueming Zhai Li Ma

Accurate prediction of wind power in wind farms plays an important role in the economic operation of wind farms and the safe operation of power grids. The randomness and volatility of wind farm winds make wind power prediction more difficult. In this paper, based on the characteristics of wind farm wind, combined with the wind speed, temperature, humidity and other related meteorological data of wind farm, the ultimate learning machine model (ELM) of wind power prediction is constructed, and the artificial fish swarm algorithm (AFSA) is used to quickly optimize. Characteristics, stepwise iterative optimization of the ω and b parameters of the extreme learning machine, the cost function is minimized, the optimized parameters are obtained, and finally the ELM prediction process is performed, thereby improving the accuracy of the ELM model prediction. The constructed model is trained and predicted by the actual meteorological data and wind power data of a wind farm. The results show that the proposed method has higher prediction than the wind power prediction model of particle swarm optimization learning machine (PSO-ELM). Accuracy.

关键词： Medium and long term wind power prediction artificial fish swarm algorithm extreme learning machine

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Tree hierarchical directed evacuation network model based on artificial fish swarm algorithm

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2019年第11期30卷

作者： Zong, Xinlu Wang, Chunzhi Du, Jiayuan Jiang, Yingli Hubei Univ Technol Sch Comp Sci & Technol Wuhan 430068 Hubei Peoples R China

With the increase of emergencies in large public places, emergency evacuation research has become an important and urgent issue. This paper first proposes a tree hierarchical evacuation network. According to the hierarchical path selection strategy, the evacuation routes are obtained and sorted by the length of route. This hierarchical path selection strategy is more realistic than using the straight line distance. An evacuation model based on hierarchical directed evacuation network is presented in this paper, and a hierarchical directed artificial fish swarm algorithm is proposed to solve the evacuation problem. The model simulates the movements of pedestrians by means of preying, swarming, following and waiting behaviors of artificial fish swarm algorithm. During the evacuation process, the effects of congestion, retrograde and blocking time on evacuation speed and route selection are considered. The simulation results show that the proposed model and algorithm can effectively improve the evacuation efficiency in a stadium, and provide scientific and reasonable path guidance.

关键词： Emergency evacuation evacuation modeling artificial fish swarm algorithm tree hierarchical directed network.

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Speech Recognition Method Based On Normalized Simplified artificial fish swarm algorithm 14

Speech Recognition Method Based On Normalized Simplified Art...

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14th International Conference on Computational Intelligence and Security (CIS)

作者： Li, Xiaofeng Qiao, Baoming Liu, Jie Xian Univ Sci & Technol Coll Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781728101699

In order to improve the classification performance of the speech recognition system, aiming at the problem that the traditional artificial fish swarm algorithm runs the late search blindness, low optimization precision and slow calculation speed, the artificial fish swarm algorithm is simplified by a certain method. A speech recognition method based on normalized simplified artificial fish swarm algorithm is proposed according to a relationship between data structure and optimization algorithm. Firstly, the speech features extracted by Mel Frequency Cepstrum Coefficient is normalized to reduce the complexity of the data structure. Secondly, the streamline operation is used to improve the iterative process of artificial fish swarm algorithm, and the modified algorithm is applied to the support vector machine parameter optimization of speech recognition. Finally, the optimized support vector machine model is used to classify and identify the normalized speech features. The experimental results show that the proposed algorithm improves the speech recognition rate by8.58% in average compared with the traditional artificial fish swarm algorithm, and the maximum increase of 15.34%, and has good anti-noise performance and generalization ability under high signal to noise ratio and large vocabulary.

关键词： speech recognition artificial fish swarm algorithm support vector machine parameters optimization data normalization

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Study on Grade Prediction of Flotation Concentrate Based on Modified artificial fish swarm algorithm

Study on Grade Prediction of Flotation Concentrate Based on ...

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2019 International Conference on Modeling, Simulation and Big Data Analysis（MSBDA 2019）

作者： Yong Zhang Hongyang Ren Zhongfeng Li University of Science and Technology Liaoning Yingkou Institute of Technology

ISBN: (纸本)9781510892606

Flotation is a process with nonlinear,large lag,strong coupling and other characteristics,concentrate grade is an important economic index to test the flotation production process,and it is difficult to establish accurate mathematical model,accurate prediction of concentrate grade is needed,which is of great significance to the quality prediction of flotation *** vector machine regression prediction was used as basic model,and modified basic artificial fish swarm algorithm(AFSA) was used to optimize parameters about the model,and GAFSA-SVM model was established to predict flotation concentrate *** initial value,crowding factor and visual field of the AFSA are improved,and the elimination mechanism is *** simulating the historical data,the improved gafsa-svm model can predict the flotation concentrate grade well,effectively improve the prediction accuracy and speed of concentrate grade,and meet the requirements of field practice.

关键词： artificial fish swarm algorithm Flotation Support vector machine

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An Improved artificial fish swarm algorithm to Solve the Cutting Stock Problem 15th

An Improved Artificial Fish Swarm Algorithm to Solve the Cut...

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15th International Symposium on Neural Networks (ISNN)

作者： Cheng, Chunying Bao, Lanying Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028043 Peoples R China Inner Mongolia Univ Nationalities Coll Math Tongliao 028043 Peoples R China

ISBN: (纸本)9783319925370;9783319925363

In order to improve the utilization rate of sheet, an improved artificial fish swarm algorithm is proposed in this paper, which improved the preying behavior and swarming behavior, meanwhile set upper and lower limit for the Congestion factor of swarming behavior. Furthermore, the proposed algorithm is used to solve the cutting stock problem. After comparing the results of the simulation experiment with the improved particle swarm algorithm in the literature and the basic artificial fish swarm algorithm, it shows that the optimal solution obtained by the improved artificial fish swarm algorithm is better than the algorithm in the literature, thus improves the utilization rate of sheet.

关键词： artificial fish swarm algorithm Cutting stock problem Improved preying behavior Improved congestion factor Utilization rate

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Optimization of Sorting Activities in Automatic Storage and Retrieval Systems Based on Improved artificial fish swarm algorithm 3

Optimization of Sorting Activities in Automatic Storage and ...

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3rd International Conference on Robotics and Automation Engineering (ICRAE)

作者： Gao, Xiaoyu Yu, Lei He, Shangyang Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China

ISBN: (纸本)9781538695944

For the problem of picking path planning in automatic storage, the traditional artificial fish swarm algorithm is improved to be used to optimize the picking system. In the algorithm, the calculation of dynamic parameters is added, so that the field of view and step size of the algorithm are dynamically changed to adapt to the convergence process of the algorithm;the chaotic model is added for initialization to obtain a better initial individual distribution. Experiments show the improved algorithm is feasible in the sorting activities in automatic storage and retrieval systems.

关键词： picking operation automatic storage artificial fish swarm algorithm travelling salesman problem algorithm optimization

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Cultured artificial fish swarm algorithm: An Experimental Evaluation

Cultured Artificial Fish Swarm Algorithm: An Experimental Ev...

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International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf)

作者： Maryam Lami Imam Busayo H. Adebiyi Habeeb Bello-Salau Gbenga A. Olarinoye Muyideen O. Momoh Department of Electrical and Electronics Engineering University of Jos Jos Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria

ISBN: (纸本)9781728107141

In this work, a Weighted Cultural artificial fish swarm algorithm (wCAFSA) which is an amendment of standard artificial fish swarm algorithm (AFSA) is proposed. This algorithm can adaptively select its parameters at every generation in order to reduce the ease at which standard AFSA falls into local optimal. We first introduce inertial weight to adaptively determine visual distance and step size of AFSA thereafter, the Situational and Normative knowledge inherent in cultural algorithm are used to develop new variants of weighted cultural AFSA (wCAFSA Ns, wCAFSA sd, wCAFSA Ns+Sd and wCAFSA Ns+Nd). A collection of sixteen (16) optimization benchmark functions are used to test the performance of the algorithms. The simulation results disclosed that all the new variants of the wCAFSA outclassed the AFSA.

关键词： artificial fish swarm algorithm Inertia weight Culture algorithm Test functions dike swarms Function algorithms local optimum step size fishes

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Weapon Target Assignment Based on Improved artificial fish swarm algorithm

Weapon Target Assignment Based on Improved Artificial Fish S...

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IEEE-Antennas-and-Propagation-Society International Symposium on Antennas and Propagation / USNC/URSI National Radio Science Meeting

作者： Ye, Fang Shao, Shijia Tian, Yuan Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538671054

Weapon-Target Assignment (WTA) problem is the key of air defense command and control. Therefore, it is an urgent problem to complete the assignment quickly and efficiently. In this paper, an improved artificial fish swarm algorithm is proposed to improve assignment rate. Based on artificial fish swarm algorithm (AFSA), particle swarm optimization (PSO) is introduced to change the individual visual of artificial fish, and genetic operator is added to avoid the local extremum trap. The proposed algorithm is validated by the concrete cooperative air defense examples. The simulation results show that the algorithm improved the computational accuracy and the rate of convergence in solving weapon-target assignment problem in air defense.

关键词： weapon-target assignment artificial fish swarm algorithm particle swarm optimization genetic algorithm

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AFSMA: An Enhanced artificial fish swarm algorithm Based on Mutuality for Community Detection 18

AFSMA: An Enhanced Artificial Fish Swarm Algorithm Based on ...

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2nd International Conference on Big Data Research (ICBDR)

作者： Wang, Yixin Xu, Wanyue Kang, Qinma Shandong Univ 180 Weihua Rd Weihai 264209 Peoples R China

ISBN: (纸本)9781450364768

Community structure is an important property of complex networks. Detecting these communities is of great significance to a wide range of applications. Community detection is an NP-hard problem having received great attentions in recent years. Modularity Q is by far the most common and well-known fitness function for measuring the quality of network division. Many optimization algorithms have been developed for community detection. In this paper, we propose a new modularity optimization method based on the artificial fish swarm algorithm, namely AFSMA. In the algorithm, mutuality is defined to represent the distance and relationship between nodes. A series of artificial fish swarm's behaviors are used to simulate the change of nodes' community labels. Experimental results on datasets of multiple real-world networks have shown the viability and effectiveness of the proposed algorithm.

关键词： Community detection artificial fish swarm algorithm Mutuality Modularity optimization

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