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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional gradient descent algorithm only uses the exponential weighted accumulation of historical gradients and does not take advantage of the local changes of gradients, which causes the optimization process to cross the global optimal solution. Even if it converges to the optimal solution, it will oscillate near the optimal solution. Using it to train graph convolutional neural network will result in slow convergence speed and low test accuracy. In this paper, the cosine similarity is used to dynamically adjust the learning rate and propose the cosine similarity gradient descent (SimGrad) algorithm. In order to further improve the convergence speed and test accuracy of the graph convolutional neural network training and reduce the oscillation, the momentum cosine similarity gradient descent (NSimGrad) algorithm is proposed combined with the momentum idea. The convergence analysis proves the regret bound of SimGrad algorithm and NSimGrad algorithm which is O(√T) . Test on the three constructed non-convex functions and experiment on four datasets combined with the graph convolutional neural network. Experimental results show that SimGrad algorithm ensures the convergence of graph convolutional neural network, and NSimGrad algorithm further improves the convergence speed and test accuracy of graph convolutional neural network training. SimGrad and NSimGrad algorithms have better global convergence and optimization ability than Adam and Nadam. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the shortcomings of arithmetic optimization algorithm (AOA) in the search process, such as local extreme points, slow convergence speed and low solution accuracy, this paper proposed a multi-strategy fusion arithmetic optimization algorithm (MFAOA). Firstly, the algorithm used the Sobol sequence to initialize the AOA population, which increased the diversity of the initial individuals and laid the foundation for the global optimization of the algorithm. Then, it reconstructed the mathematical optimizer acceleration function (MOA) to weigh the proportion of the global search and the local development process. Finally, it used the chaotic elite mutation strategy to improve the algorithm's over-dependence on the current optimal solution and enhanced the algorithm's ability to jump out of the local extremum. The paper used 12 general benchmark functions and part of CEC2014 test functions for experimental simulation, and the results show that MFAOA has a significant improvement in solution accuracy and convergence speed. Besides, the algorithm introduced two engineering examples for optimization, which verifies the feasibility of MFAOA in engineering optimization problems. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems in the iterative process of seagull optimization algorithm ( SOA), such as local optimization, slow convergence speed and low optimization accuracy, this paper proposed a golden sine guide and sigmoid continuous seagull optimization algorithm( GSCSOA) . In the seagull migration stage, the algorithm used the sigmoid function as a nonlinear convergence factor to guide the seagull search process, so that the algorithm maintained a stronger global optimization ability in the early stage and converged faster in the later stage. In the seagull rushing stage, it introduced the idea of Taboo search, so that the seagulls always moved to the area with higher confidence, and the optimal position continued to change in one iteration, which improved the optimization accuracy. After that, it used the golden sine mechanism to guide the population position to update, which narrowed the search range, and improved the local optimization ability. Finally, this paper used 12 test functions and CEC2014 function set to test the improved algorithm, and the results prove that the improved seagull algorithm has better convergence speed and accuracy than original algorithm and other algorithms. [ABSTRACT FROM AUTHOR]

In order to optimize the performance of the multi-verse algorithm to solve the optimal value of function, this paper proposed a global optimization multiverse algorithm（G-MVO） with an improved search mechanism. Aiming at the defects of the standard algorithm, which was easy to fall into local optimum and premature convergence due to the single search mechanism, this paper proposed three learning strategies to enhance the algorithm performance: through multi-strategy interaction and cooperation to reduce the complexity of the algorithm and improve the accuracy of the solution, designing adaptive parameters to dynamically choose the best strategy and optimizing the performance of the algorithm globally. In order to verify the effectiveness of the G-MVO algorithm, simulation experiments were carried out on 8 benchmark functions of different dimensions. Experimental results show that the proposed algorithm has better solution precision and convergence speed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The optimization algorithm of concrete pump truck global power matching technology has poor solution quality, this paper proposed a condition adaptive global power matching strategy based on advanced particle swarm optimization. Firstly, based on the characteristic data of engine and hydraulic pump, a pumping system model on MWorks that integrated five major modules of hydraulic, mechanical, control, load and power is builted; Then, the advanced particle swarm optimization algorithm is used for global matching optimization and output the economic matching value of parameters under different operation requirements; Finally, a pumping system simulation platform is established to analyze the energy-saving effect of different power matching strategies under typical conditions. The results show that compared with the traditional strategy, strategy proposed saves fuel by 5.86%, 5.24% and 1.74% under light, medium and heavy operation, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering that the traditional simultaneous pickup and delivery mode is single and cannot cope with the complex and changeable reality needs, this paper proposed VRPD-SPD. Firstly, the method used a hybrid integer linear programming model to minimize the total cost between vehicles and drones. Secondly, the algorithm made a TSHAG algorithm. In the first stage, the greedy algorithm combined with the saving algorithm to generate the initial solution. In the second stage, in order to optimize initial solution, the solving process used improved genetic algorithm, which constructed the multivariate encoding mode to boost the decoding efficiency, improved the crossover operator to increase the search space of the neighborhood solution, and designed a new mutation operator to improve the global optimization performance of the algorithm. Finally, the experimental results show that the proposed TSHAG algorithm can solve VRPD-SPD problem effectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aimed at the problems such as slow convergence speed, low accuracy and easy to fall into local optimality, an improved sparrow search algorithm based on multiple strategies（ISSAMS） is proposed in this paper. Circle chaotic mapping was introduced to initialize the population, increase the diversity of the population, and improve the global search ability. Using sine and cosine search strategy was used to update the location of the finder, select the best location, enhance the local search ability, and avoid falling into the global optimization. Firefly disturbance was added to update the optimal individual position, search for feasible solutions, and improve the local search ability and search speed. In order to verify the effectiveness of the improved algorithm, five reference functions were selected for simulation experiments, among which three functions were single-peak functions and two functions were multi-peak functions, and compared with genetic algorithm, grey wolf algorithm, particle swarm optimization algorithm and sparrow search algorithm. The simulation results show that the improved sparrow search algorithm based on multiple strategies has the ability to jump out of the local optimal solution, the convergence speed is faster, the accuracy is higher, and the overall performance is better than the other four algorithms. By applying this improvement to the threshold and weight of the optimized BP neural network, the error of the BP model optimized by no improved sparrow search algorithm is reduced by 14. 73%. Compared with the BP model optimized by the other three algorithms, the average absolute percentage error of the optimized model based on the multi-strategy improved sparrow search algorithm is the lowest and has the best effect. The effectiveness of the improved algorithm is further verified. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposed and discussed a genetic algorithm based on virtual machine migration model in cloud data centers called GA-VMM. It took into account several important parameters to frame the objective function for the virtual machine migration which overwhelmed the old strategies and thus it made the virtual machine migration more reasonable and fair. It constructed the mathematics model of power consumption and live migration cost of virtual machine in GA-VMM. It designed a global optimization genetic algorithm for how virtual machine migrated and placed. Using a corporation cloud data centers servers, this paper did some simulation experiments. The experimental results show that GA-VMM can effectively reduce the number of physical machine usages and virtual machines migrations compared with the common strategy. It had also obtained the fewer SLA violation and low energy consumption of data centers in our test. [ABSTRACT FROM AUTHOR]

This paper proposed curve adaptive and simulation annealing grasshopper optimization algorithm to avoid the disadvantages that the grasshopper optimization algorithm was easy to fall into local optimum, slow convergence speed and poor search accuracy. First of all, this paper introduced the adaptive curve to replace the linear adaptation of the grasshopper optimization algorithm key parameter, which improved the global search ability of the algorithm. Then, on this basis, this paper introduced the simulated annealing algorithm, which had a certain probability of receiving the inferior solution of the grasshopper algorithm to jump out of local optimization and achieve global optimization. Adaptive reduction the range of random solutions of grasshopper position in simulated annealing was beneficial to further improve the ability of grasshopper algorithm's exploitation. Through test function, the experimental results show that the improved new algorithm has higher quality and better convergence speed. [ABSTRACT FROM AUTHOR]

In view of the traditional genetic algorithm in the process of function optimization tended to fall into the local optimal value, slow convergence efficiency and other shortcomings, this paper proposed an adaptive genetic algorithm, which took into account the diversity of population fitness, and adaptively adjusted the crossover probability and mutation probability of the genetic algorithm. To speed up convergence rates, in the aspect of selection operator, this paper combined the introduced selection operator with the optimal preservation strategy. In order to keep the population size constant during the genetic operation, this paper put forward a strategy of keeping parents. Simulation experiments show that compared the improved adaptive genetic algorithm with classical genetic algorithm GA and the IAGA, the improved adaptive genetic algorithm has a great improvement in the convergence rate and optimal solution. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The secondary energy on board has been gradually replaced by electrical power in more electric aircraft, effectively improving the fuel economy, reliability and maintainability of the aircraft, and has become an important direction of the aviation technology. As the core of the secondary energy system on board, the power system plays a key supporting role in the development of more electric aircraft. The innovative development of the power system and related technologies are the necessary basis to realize the improvement and global optimization of the aircraft comprehensive performance. Based on the concepts and characteristics of more electric aircraft, this paper analyzes and compares the electric power system architecture of three typical more electric aircraft, summarizes the key technologies supporting the development of more electric aircraft electric power system, and discusses the development trend of high voltage, direct current and intelligent more electric aircraft electric power system in the future. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithm is a stochastic global optimization algorithm, but it is easy to fall into local optima and premature. In order to overcome its defects, a diversity driven adaptive particle swarm optimization（DDA-PSO） algorithm is proposed in this paper. The algorithm includes the attraction stage and the driving stage. In the attraction stage, the linear decreasing inertia weight mechanism is used to accelerate the particle convergence, and in the driving stage, the diversity driving speed strategy is used to improve the population diversity. The two stages adaptively switch to each other, the particles can jump out of the local optima and prevent premature, the algorithm obtains an adaptive balance between the exploration and exploitation. The experimental results show that DDA-PSO algorithm improves the convergence speed and accuracy, and the global search ability is significantly improved. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Green crop is generally used as the foreground in image segmentation of agricultural visual navigation system because of its obvious chromaticity difference from the basic image background. However, for the segmentation of maize stubble row, there are many backgrounds in the field harvested by combine harvester, such as naked land surface, maize residues, which has little color difference with the maize stubble row. To achieve accurate and quick segmentation of maize stubble row, an image segmentation approach of maize stubble row based on genetic algorithm and threshold filtering noise was proposed in this paper. Firstly, the RGB color space was selected to accomplish the segmentation, which is a basic color space and widely used in machine vision automatic guidance systems. Secondly, to reduce the difficulty of image segmentation, the region of interest (ROI) was selected by calculating the maximum of column gray value accumulation. Thirdly, the gray-scale image of the maize stubble row was obtained through the optimized gray-scale operator. Besides, the genetic algorithm was often used in global optimization, which was used to optimize the gray-scale operator. Specially, the maize stubble row image’s pixel was divided into 3 classes: land surface, residues in rows and stubble tip incision, and the 3 classes were selected as the sample to optimize the gray-scale operator by genetic algorithm. Then, the segmentation of single threshold method was used to segment the gray-scale image. Furthermore, the segmentation of single threshold method is widely used in image segmentation because of its high efficiency and convenience. Lastly, the morphological corrosion treatment (MCT) and threshold noise filtering algorithm (TNFA) were applied to guarantee the integrity of the maize stubble row region and the noise points on both sides of the maize stubble row removal. In order to verify the effect of the method proposed in this paper, 200 test images were captured from Zhuozhou Experimental Station of Conservation Tillage Research Center of Ministry of Agriculture in Baoding City, China in each October during 2014－2016. The acquisition was often on sunny day, aiming at collecting images under different natural lighting conditions, different positions in maize stubble row field. The results showed that the average relative object area error (ARAE) by our method was only 24.68%, while the AREA of the iteration method and the OTSU method were 90.67% and 86.42%, respectively. The average processing time of a test image based on this paper presented algorithm was 0.16s, which was much more time-consuming than the OTSU method (0.07s), while the processing time of our method was less than the iteration method (0.25s). Therefore, the method presented in this paper achieved better performance than other methods in maize stubble row segmentation, and was effective to segment the maize stubble row in the complicated backgrounds. The presented method can provide precise basis for detection of guidance line in maize straw covering field. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the global optimization problem, this paper proposed a new intelligent algorithm called RGSO, based on SVDD and the root algorithm existed. The root system differentiated into the taproot and the lateral root group. Inspired by this growth behavior, this paper described the growth behavior of the taproot tips based on SVDD. The algorithm constructed the root growth model, and used the point of the highest concentration of nutrients in the soil as the target of global optimization. It analyzed the mathematical model of RGSO, and proved its convergence theoretically. In the experiment, this paper compared RGSO with the other three advanced algorithms, and tested the optimization effect with different parameters. The result of the experiment verified the convergence and effectiveness of RGSO, and it indicates that RGSO is an effective algorithm to solve global optimization problem. [ABSTRACT FROM AUTHOR]

The maneuvering parameter identification of non-cooperative atrcraft is studied when the known information is sparse orbit elements. In this paper, a maneuver parameter identification method based on orbit inveryion and its corresponding solution algorithm are proposed. Firstly, the orbit invertion model and the maneuvering parameier identification optimization model are established. Then, a double optimization algorithm of "global optimization plus local correction" is proposed. Finally, the maneuvering parameter identification method proposed is verified by simulation. The results show that the double optimization method can effectively solve the optimization model, and the proposed method can effectively identify maneuvering parameiers within an acceptable error range. The research provides an idea for maneuvering identification of non-cooperative aircraft with lifted information, and ensures the on-orbit safety of spacecraft to a certain extent. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Liaoning Technical University (Natural Science Edition) / Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of Liaoning Technical University (Natural Science) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Taxi becomes an important supplement to urban public transportation because of its convenient, fast and comfortable features. Focusing on the issues of unreasonable resources distribution, high load rate and inefficient assignment of taxies, this paper proposes an approach to intelligent taxi dispatching based on artificial fish swarm algorithm (AFSA). Basic functions of foraging, clustering, rearing in AFSA are improved in the paper and an optimization strategy of setting state threshold is presented. Also, some key parameters, i.e. vision, trynum, crowd, maxgen and step, are analyzed under simulation experiments, which are related to optimization capabilities of taxi dispatching algorithm. The results show it would be helpful to make reasoning planning and allocation of global optimization on taxi resources. [ABSTRACT FROM AUTHOR]

Aiming at solving the problem of convergence defect in the traditional gray wolf optimization algorithm ( GWO), this paper proposed an adaptive gray wolf optimization algorithm ( AGWO) with hybridization strategy. The algorithm firstly introduced a non-linear convergence factor to balance the global search and local development of the algorithm. Secondly, it introduced a genetic hybrid strategy, which crossed the gray wolf population in pairs with a certain probability to produce new individuals, thereby effectively enhancing the diversity of the gray wolf population. At the same time, in order to avoid the algorithm falling into the local optimal solution in the later stage, inspired by the manta ray foraging strategy, the manta ray foraging strategy was introduced and a dynamic adaptive adjustment factor was added to adjust the diversity of the population, which effectively improved the convergence accuracy of the algorithm and global optimization performance. Numerical experiments of selecting 11 benchmark test functions in CEC2014 demonstrate that the proposed algorithm achieves better performance of higher convergence speed and optimization. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithm-SVM is a typical framework of multi-classification evaluation model, which has important theoretical and practical significance in multi-classification comprehensive decision-making. Therefore, this paper proposed a multi-classification evaluation algorithm of an improved whale optimization algorithm using Lévy flight and elite opposition-based learning SVM（LFEO-BWOA-SVM）. It used Lévy flight strategy instead of spiral trajectory strategy to update position information effectively, and could overcome the deficiency of whale optimization algorithm which was easy to fall into local optimization. It introduced the elite opposition-based learning mechanism to increase population diversity and improved the global optimization ability of whale optimization algorithm. The simulation results show that the classification accuracy of LFEO-BWOA-SVM algorithm is 17.84% and 4.51% higher than that of traditional SVM and BP neural network, the accuracy is 98.73%, and the training time is 9.34% and 84.94% shorter than that of standard WOA-SVM and PSO-SVM, respectively. The experimental results show that the optimization ability and convergence speed of LFEO-BWOA-SVM algorithm are obviously improved, and the accuracy and rapidity are good. [ABSTRACT FROM AUTHOR]

In order to solve the problems of easily fall into the local optimal value, low convergence accuracy and slow convergence speed in the basic slime mould algorithm（SMA）,this paper put forward an improved ISMA based on elite opposition-based learning and quadratic interpolation. Elite opposition-based learning strategy was conducive to improve the diversity and quality of population, improved the global optimization performance and convergence accuracy of the algorithm. It used quadratic interpolation to generate new individuals and fitness evaluation to update the global optimal solution was propitious to enhance the local development ability, reduced the convergence time and made the algorithm jump out of the local extremum. By solving multiple unimodal, multi-modal and high-dimensional test functions, the results show that the ISMA combined with the two strategies has higher optimization accuracy, optimization speed as well as robustness. [ABSTRACT FROM AUTHOR]

This paper proposed improved particle swarm optimization algorithm( GB_PSO ) for robot path planning to address the problems of easy to fall into local misconceptions when solving robot path planning by a single intelligent optimization algorithm. The algorithm took on the PSO as the main body,due to the GA and the BFO algorithm update strategies was subject to different environmental influences, fitting two environmental parameters. Then the correlation between particles and different environmental parameters calculated to classify the particle swarm into two classes, and local optimization enhanced in parallel by selection, crossover, and variation operators of GA and convergence operations of BFO, respectively . Finally the improved p article swarm update formulation used to achieve the optimization of global and local paths of the robot. The experimental results show that the GB_PSO algorithm for path planning improves the local and overall search capability. The path planning is fast and short distance, while having stronger robustness. [ABSTRACT FROM AUTHOR]

Differential evolution ( DE) algorithm has some disadvantages, such as slow convergence speed, low convergence precision and easy to fall into local optimal solution in the early stages of the evolution. The paper proposed an improved adaptive differential evolution (IADE) algorithm by improving the mutation equation of DE and introducing a new control parameters adaption strategy. In the process of the evolution,the control parameters will be dynamically adjusted by comparing individual fitness with average fitness of the parent population. Meanwhile, the paper chose the ten standard functions commonly used for the comparison of optimization algorithm to perform the comparative test of IADE and the other improved DE algorithms, and the experimental results show that IADE algorithm not only can significantly improve the convergence speed and convergence precision, but also has very good robustness, so that IADE algorithm can meet the requirements for the real-time .accuracy and stability of process optimization. [ABSTRACT FROM AUTHOR]

Objectives: Traversability analysis is one of the necessary parts for rovers on extraterrestrial surface to explore unknown environment. Methods: In this paper, we propose a data⁃driven method for traversability analysis for rovers on extraterrestrial surface. Based on the inputting multi⁃dimensional terrain in⁃ formation, the proposed method models traversability analysis as a semantic segmentation problem, which can explicitly compute a traversability map of this circumstances for a specific rover. Meanwhile, we provide an algorithm for generating training dataset for the rover. We first run the rover in the field to collect di⁃ rected traversability results at certain positions，and then fulfill the undirected traversability map with these results by converting this problem into a global optimization problem, since undirected traversability map is more intuitive and straightforward for path planning. We can get the dataset for a specific rover by linking this map with the data of environment. In order to get the data more efficiently, we design an algorithm to generate virtual extraterrestrial terrains randomly and to simulate the running of a specific rover.Results:We generate a set of visible multi⁃dimensional terrain information and perform traversing test in virtual environment, which is used for generating traversibility labels in the optimization method. Based on the terrain information and labels, we train a U⁃Net⁃like network for predicting labels according to the given multi⁃dimensional information, and the network performs well on test dataset with the accuracy of 93.8% on average. Conclusions:The proposed data⁃driven method for traversability analysis is effective in virtual environment. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Swarm intelligence algorithm has become a hotspot in the field of evolutionary algorithm because of its strong dynamic optimization ability and simple implementation approach. The choice of control parameters has a great influence on the optimization performance of the algorithm. Firstly, this paper studied the particle swarm parameters from the point of mathematical derivation, then proposed an improved algorithm with inverse thinking curve increment strategy, which fitted the evolution formula of the particle itself. Finally, it verified that the algorithm has the following two outstanding advantages. It can effectively avoid the problem of precocity. In dealing with the problem of dimensional disaster, the optimization performance is stronger and has a good balance between global and local optimization performance. The algorithm is simple and can effectively solve the problem of low robustness and tedious manual parameter adjustment. [ABSTRACT FROM AUTHOR]