| 刘俊毅,汪洋.多样性种群增强遗传算法机器人全局路径规划研究[J].电子测量与仪器学报,2025,39(9):39-54 |
| 多样性种群增强遗传算法机器人全局路径规划研究 |
| Research on genetic algorithm for robot global path planning withdiversity population enhancement |
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| DOI： |
| 中文关键词: 移动机器人 全局规划 路径规划 遗传算法 混沌映射 差分扰动 Levy飞行 交叉变异概率 |
| 英文关键词:mobile robot global planning path planning genetic algorithm chaotic mapping differential perturbation Levy flight crossover and mutation probability |
| 基金项目:江苏省自然科学研究面上基金(20KJB530008)、国家科学信息技术部研究中心“十四五”课题基金(KXJS71057)、教育部“十四五”教育科研规划课题基金(JXKY24391)项目资助 |
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| 中文摘要: |
| 为解决传统遗传算法面临局部最优解和迭代速度慢等问题，从多个方面对遗传算法进行了改进。首先，将传统的8方向搜索扩展为24邻域16方向，以增强全局搜索能力；引入PT(piecewise and tent)混沌映射融合策略，通过Piecewise混沌映射生成的序列作为Tent混沌映射参数，以提升种群多样性；其次，结合莱维（Levy）飞行策略避免局部停滞，并提出新的越界粒子处理策略，以防初始化种群越界；接着，设计了全新配对交换和差分扰动机制，防止优良个体丧失导致陷入局部最优；最后，提出了一种新的压力等级拆分选择机制和自适应交叉变异概率调整算子，通过调整系数解决选择压力过大问题，采用非线性指数函数调整交叉选择概率，以避免早期发散，并通过互补调整变异概率，扩大搜索空间，减少收敛震荡。实验结果表明，所提方法相比传统遗传算法及其他改进算法，显著提高了路径规划性能，路径长度分别减少5.13%和2.06%，验证了其在机器人路径规划中的优越性与实用性。 |
| 英文摘要: |
| To address the issues of local optima and slow convergence speed encountered by traditional genetic algorithms, several improvements have been made to the genetic algorithm in this paper. Firstly, the conventional 8-direction search is extended to a 24-neighborhood, 16-direction search to enhance the global search ability. Secondly, a Piecewise and Tent (PT) chaotic mapping fusion strategy is introduced, where the sequence generated by the Piecewise chaotic mapping is used as the parameter for the Tent chaotic mapping to improve population diversity. Furthermore, the Levy flight strategy is integrated to avoid local stagnation, and a new strategy for handling out-of-bounds particles is proposed to prevent the initialization population from exceeding boundaries. A novel pairing exchange and differential perturbation mechanism is then designed to prevent the loss of good individuals, which may lead to the algorithm getting stuck in local optima. Lastly, a new pressure level splitting selection mechanism and an adaptive crossover and mutation probability adjustment operator are proposed. Coefficients are adjusted to resolve the issue of excessive selection pressure, and a nonlinear exponential function is used to adjust the crossover selection probability to avoid early divergence. Additionally, complementary adjustments to mutation probabilities are introduced to expand the search space and reduce convergence oscillations. Experimental results show that the proposed method significantly improves path planning performance compared to traditional genetic algorithms and other improved algorithms, with path lengths reduced by 5.13% and 2.06%, respectively. The superiority and practicality of the method in robot path planning are validated. |
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