Ant colony algorithm is easy to fall into local optimum and its convergent speed is slow in
solving mobile robot path planning. Therefore, an ant colony algorithm based on angle …

The traditional ant colony algorithm has some problems, such as low search efficiency, slow
convergence speed and local optimum. To solve those problems, an adaptive heuristic …

To improve the finding path accuracy of the ant colony algorithm and reduce the number of
turns, a jump point search improved ant colony optimization hybrid algorithm has been …

Aiming at the problems of traditional ant colony algorithm (ACO) applied to mobile robot path
planning, such as easy to fall into local optimization, low search efficiency, poor …

In this paper, we address the problem that in path planning research, the traditional ant
colony algorithm suffers from slow convergence and is easily trapped in local optimal …

The classical ant colony algorithm has the disadvantages of initial search blindness, slow
convergence speed and easy to fall into local optimum when applied to mobile robot path …

In order to overcome the limitation of the current path planning algorithms for mobile robot, a
fusion algorithm based on ant colony optimization and genetic optimization was proposed …

As the key point for auto-navigation of mobile robot, path planning is a research hotspot in
the field of robot. Generally, the ant colony optimization algorithm (ACO) is one of the …

Aiming at the problems of traditional ant colony optimization algorithm in path planning, such
as slow convergence, easy to fall into local optima, and too many inflection points, an …

The basic ant colony algorithm for mobile robot path planning exists many problems, such
as lack of stability, algorithm premature convergence, more difficult to find optimal solution …