融合头脑风暴和注意力机制的改进蚁群路径规划算法研究

陈嘉航; 李媛媛

doi:doi:10.3969/j.issn.1671-637x.2023.04.001

电光与控制, 2023, 30 (4): 1, 网络出版: 2023-06-12

融合头脑风暴和注意力机制的改进蚁群路径规划算法研究

An Improved Ant Colony Path Planning Algorithm Combining Brainstorming with Attention Mechanism

论文大纲

陈嘉航李媛媛

作者单位

上海工程技术大学, 上海 201000

蚁群算法路径规划头脑风暴注意力机制自适应t分布 ant colony algorithm path planning brainstorming attention mechanism adaptive t-distribution

摘要

蚁群算法是一种智能优化算法, 具有鲁棒性强、反馈信息精准、分布式计算能力强等优点, 被广泛应用于移动机器人的路径规划。针对原算法存在收敛速度慢、易陷入局部最优等问题, 提出了一种改进蚁群路径规划算法。首先, 融合头脑风暴思想对解集进行更新变异, 在加快收敛的同时保证算法的多样性。其次, 利用局部路径注意力机制提取较好的路径段, 提高寻优效率, 且在信息素注意力机制中加入了自适应t分布, 避免算法陷入局部最优。新的信息素更新方式可以促进算法的全局搜索, 并且保障算法的收敛速度。最后, 在Matlab软件中进行了静态环境下的仿真实验, 验证了该算法的有效性和可行性。

Abstract

Ant colony algorithm is an intelligent optimization algorithm, which has the advantages of strong robustness, accurate feedback information and strong distributed computing ability.It is widely used in mobile robot path planning.The original algorithm converges slowly and is prone to falling into local optimization.To solve the problems, an improved ant colony path planning algorithm is proposed.Firstly, the solution set is updated and mutated by integrating the idea of brainstorming, so as to speed up the convergence and ensure the diversity of the algorithm.Secondly, the local path attention mechanism is used to extract better path segments, so as to improve the optimization efficiency, and the adaptive t-distribution is added to the pheromone attention mechanism, so as to avoid falling into local optimization.The new pheromone updating method can promote the global search of the algorithm and ensure the convergence rate of the algorithm.Finally, simulation experiments in static environment are carried out in Matlab software, which have verified the effectiveness and feasibility of this algorithm.

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陈嘉航, 李媛媛. 融合头脑风暴和注意力机制的改进蚁群路径规划算法研究[J]. 电光与控制, 2023, 30(4): 1. CHEN Jiahang, LI Yuanyuan. An Improved Ant Colony Path Planning Algorithm Combining Brainstorming with Attention Mechanism[J]. Electronics Optics & Control, 2023, 30(4): 1.

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