基于平衡鲸鱼优化算法的无人车路径规划
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南京理工大学

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TP242

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国家自然科学基金项目(71971116),国家重点研发计划(2018YFB1601100/2018YFB1601101)


Path planning of unmanned ground vehicle based on balanced whale optimization algorithm
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Nanjing University Of Science And Technology

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    摘要:

    针对地面无人车辆路径规划问题的特点,提出基于和声二次优化的平衡鲸鱼算法求解最优路径.首先,利用和声搜索算法二次优化改善种群质量和全局探索能力,依据解的适应性进行微调,提高求解精度;然后,引入动态平衡策略和种群重构机制,跟踪种群最优解状态以协调全局探索与局部开发能力,出现优化停滞时重构种群增加多样性,避免陷入局部最优;最后,基于不同环境进行仿真实验,与多种算法进行了对比分析,证明了所提算法的可行性和有效性,为鲸鱼优化算法在路径规划问题中的应用提供一种新思路.

    Abstract:

    According to the characteristics of the path planning problem of ground unmanned vehicles, a balanced whale algorithm based on harmonic quadratic optimization is proposed. Firstly, the optimization of harmony search algorithm is used to improve the population quality and global exploration ability, and fine-tuning is carried out according to the adaptability of the solution to improve the accuracy; secondly, the dynamic balance strategy and the population reconstruction mechanism are introduced to track the optimal solution state of the population to coordinate the global exploration and local development ability, and when the optimization stagnates, the reconstructed population increases the diversity, avoiding falling into the local optimization. Finally, simulation experiments are carried out based on different environments, and compared with a variety of algorithms, which proves the feasibility and effectiveness of the proposed algorithm, providing a new idea for the application of whale optimization algorithm in path planning.

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  • 收稿日期:2020-04-13
  • 最后修改日期:2020-09-08
  • 录用日期:2020-09-27
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