Characteristics and Optimization Strategies of A* Algorithm and Ant Colony Optimization in Global Path Planning Algorithm

被引：5
|
作者：
Ni, Yun [1 ]
Zhuo, Qinghua [2 ]
Li, Ning [3 ]
Yu, Kaihuan [2 ]
He, Miao [2 ]
Gao, Xinlong [2 ]
机构：
[1] Jinhua Polytech, Coll Mech & Elect Engn, Jinhua 321017, Peoples R China
[2] Zhejiang Normal Univ, Coll Engn, Jinhua 321000, Peoples R China
[3] Zhejiang Normal Univ, Xingzhi Coll, Jinhua 321000, Peoples R China
来源：
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2023年 / 37卷 / 03期
关键词：
Path planning; A* algorithm; Ant Colony Optimization; heuristic function; pheromone function; STAR ALGORITHM;
D O I：
10.1142/S0218001423510060
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A* algorithm and ant colony optimization (ACO) are more widely used in path planning among global path planning algorithms. The optimization process is analyzed and summarized from the principles and characteristics of the two algorithms, A* algorithm is mainly optimized in terms of point selection and improvement of heuristic function; and ACO is mainly investigated in terms of transfer probability and pheromone positive feedback for improvement and optimization. Taking a single algorithm solving complex optimization problems difficulties into consideration, a splitting strategy can be used. So that local path or intelligent path optimization algorithms are incorporated in global path planning to improve search efficiency and optimization quality.
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页数：14
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