A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning

Chen, Zeqiu; Zhou, Jianghui; Sun, Ruizhi; Li, Kang

doi:10.1007/s00500-020-05510-6

Cited by 20 publications

(6 citation statements)

References 62 publications

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“…In computer graphics, the automatic setting of camera path is an important topic having a long history [10,25,8]. Here, a relatively simple algorithm for the camera agent motion is used because the camera agent is required to autonomously respond to ever-changing simulation data.…”

Section: Related Workmentioning

confidence: 99%

Autonomous camera for agent-based in situ visualization

Wang

Kageyama

2022

JASSE

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For effective in situ visualization in high performance computing, a viewpoint should be placed close to a critical spot or volume-of-interest (VOI). In order to track unpredictable motions of VOI in simulations, we propose introducing agent-based modeling to the in-situ visualization. In this method, agents are autonomous cameras, and their environment is the simulation. As a demonstration experiment of the agent-based in situ visualization, we put a camera agent to 3D cellular automata. The camera agent successfully tracks a VOI of cells in highly complex time development.

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Section: Related Workmentioning

confidence: 99%

Autonomous camera for agent-based in situ visualization

Wang

Kageyama

2022

JASSE

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“…It is a method to find the optimal solution or approximate optimal solution to a complex problem by simulating the natural evolution process. It has been used in neural networks 22 , combinatorial optimization 23 , artificial intelligence 24 , 25 , genetic programming 26 , data mining 27 and other fields. The optimization principle is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Qiu

Sun

et al. 2022

Sci Rep

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Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures.

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“…e rst category is swarm optimization algorithms, including particle swarm algorithms and [7,8] ant colony algorithms [9,10]. e second category has evolutionary algorithms, including genetic algorithms and [11,12] immune algorithms [13]. Another class for other algorithms is mainly the A-star algorithms [14,15] and the arti cial potential eld method [16,17].…”

Section: Introductionmentioning

confidence: 99%

Path Optimization of Agricultural Robot Based on Immune Ant Colony: B-Spline Interpolation Algorithm

Kai

Wang

et al. 2022

Mathematical Problems in Engineering

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This study presents a path planning algorithm based on immune ant colony B spline interpolation to eliminate path duplication and corner inflection points, increase the smoothness of the agricultural robot’s path trajectory, and enhance the path planning performance. Firstly, this study improves the convergence speed of the algorithm by introducing an adaptive factor that obeys the normal distribution to optimize the path heuristic function. Secondly, to strengthen the variety of the target path search, an immune algorithm was utilized to perform a global investigation of the appropriate routes and assign the initial pheromones of the ant colony. Then, the adaptive judgment factor is introduced based on the incentive degree function and the pheromone evaporation coefficient, and the update formula are adjusted to promote the algorithm’s flexibility while suppressing the optimal regional problem. Finally, the pathways are processed using a cubic B spline interpolation method to remove the edges and minimize the peak inflection points. To verify the specific optimization performance of the algorithm in this paper, we have selected the same type of improved algorithm as a comparison and tested it in a raster map model. The simulation results demonstrate that the values of the proposed algorithm paths and the standard deviation of the tracks are smaller than the comparison algorithm, which indicates the higher performance of the algorithm in searching the optimal routes and more flexible and accurate search. Compared with the comparison algorithm, the average time-consuming reduction rate of the algorithm in this paper is between 38% and 45% in different grid maps, and the number of turns is reduced by 33% to 58%. It meets the requirements of the actual work on the robot.

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A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning

Cited by 20 publications

References 62 publications

Autonomous camera for agent-based in situ visualization

Autonomous camera for agent-based in situ visualization

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Path Optimization of Agricultural Robot Based on Immune Ant Colony: B-Spline Interpolation Algorithm

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