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

Abstract

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.

A*–Ant Colony Optimization Algorithm for Multi-Branch Wire Harness Layout Planning

“…There are relatively few works on the path planning of multi-branch cables. Non-deterministic search algorithms exhibit good performance in the problem of the automatic layout of the wire bundles [11][12][13]. Existing research primarily focuses on the automatic layout planning of single-branch cables.…”

Path Planning Algorithm Based on Obstacle Clustering Analysis and Graph Search

“…Global path planning is a type of static planning that determines an optimal path for a known global map [3]. Conventional global path planning algorithms include the A-Star (A*) [4][5][6] algorithm, D-Star (D*) [7] algorithm, the rapidly exploring random tree (RRT) [8] algorithm, the genetic algorithm (GA) [9], and the ant colony algorithm optimization (ACO) [10] method. Among them, A* is widely used; it employs a symmetric search process that progresses from the current node to surrounding nodes.…”