The CAD of Group Animation based on Firefly Algorithm and Ant Colony Algorithm Fusion Algorithm

The firefly algorithm is a recently developed optimization algorithm, which is suitable for solving any kind of discrete optimization problems. This is an algorithm inspired from the nature. In this paper, a firefly algorithm is proposed to solve random traveling salesman problem. The solution to this problem is already proposed by the algorithms like simulated annealing, genetic algorithms and ant colony algorithms. This algorithm is developed to deal with the issue of accuracy and convergence rate in the solutions provided by those algorithms. A comparison of the results produced by proposed algorithm with the results of simulated annealing, genetic algorithms and ant colony algorithm is given. Finally, the effectiveness of the proposed algorithm is discussed.

Download Full-text

UAV Path Planning of Combining Ant Colony and Beetle Antennae Algorithm Using Intelligent Wireless Communication

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/2/022058 ◽

2021 ◽

Vol 2083 (2) ◽

pp. 022058

Author(s):

Le Xu ◽

Wenlong Zhao

Keyword(s):

Path Planning ◽

Ant Colony Algorithm ◽

Interpolation Method ◽

Spline Interpolation ◽

Three Dimensional ◽

Ant Colony ◽

Fusion Algorithm ◽

Third Order ◽

Environment Model ◽

Speed Up

Abstract In order to settle the problem of UAV path planning under mountain, an algorithm which based on the combination of ant colony algorithm and beetle antennae algorithm is proposed. Three dimensional environment model is established and objective function is constructed. It used ant colony algorithm to initialize the search path and the particle coordinates of all the next steps are updated by the beetle antennae algorithm. The improved algorithm adopted a new step update rule to speed up the convergence of the algorithm and used third-order B-spline interpolation method to smooth the path. Simulation results show that improved fusion algorithm has faster convergence speed and high stability by comparing with other algorithms under the same conditions, which verifies its effectiveness.

Download Full-text

Research on path planning based on new fusion algorithm for autonomous vehicle

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420911235 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142091123

Author(s):

ChaoChun Yuan ◽

Yue Wei ◽

Jie Shen ◽

Long Chen ◽

Youguo He ◽

...

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Potential Field ◽

Ant Colony Algorithm ◽

Autonomous Vehicle ◽

Ant Colony ◽

Fusion Algorithm ◽

Simulink Simulation ◽

Initial Stage ◽

Pheromone Concentration

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.

Download Full-text

Optimization of one-dimensional wire cutting with variable length based on genetic ant colony algorithm

MATEC Web of Conferences ◽

10.1051/matecconf/202133602011 ◽

2021 ◽

Vol 336 ◽

pp. 02011

Author(s):

Xin Xia ◽

Defu Wan

Keyword(s):

Genetic Algorithm ◽

Ant Colony Algorithm ◽

Raw Materials ◽

Optimal Solution ◽

Ant Colony ◽

Utilization Rate ◽

Fusion Algorithm ◽

One Dimensional ◽

Experimental Parameters ◽

Wire Cutting

This paper presents an algorithm of one-dimensional wire cutting based on genetic algorithm and ant colony algorithm. Firstly, the dominant solution is screened out by genetic algorithm and transformed into the initial accumulation of pheromone in ant colony algorithm, and then the ant colony algorithm is used to adjust the dominant solution of genetic algorithm to obtain the approximate optimal solution. The experimental results show that the convergence rate of the fusion algorithm is faster than that of the ant colony algorithm, and the utilization rate of raw materials is higher than that of genetic algorithm. In addition, the optimal parameters are obtained by adjusting the experimental parameters of the fusion algorithm.

Download Full-text

Path Optimization for Automatic Guided Vehicle Based on Fusion Algorithm of Particle Swarm and Ant Colony

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1452 ◽

2012 ◽

Vol 182-183 ◽

pp. 1452-1457 ◽

Cited By ~ 3

Author(s):

Yuan Bin Hou ◽

Yi Qian Yuan ◽

Bai Ping Li

Keyword(s):

Ant Colony Algorithm ◽

Optimal Path ◽

Particle Swarm ◽

Ant Colony ◽

Path Optimization ◽

Particle Swarm Algorithm ◽

Ant System ◽

Fusion Algorithm ◽

Automatic Guided Vehicle ◽

Swarm Algorithm

Aim at search precocity of particle swarm algorithm and slow convergence speed problem for ant colony algorithm, in the automatic guided vehicle path optimization a path optimization algorithm is proposed, which is fused by particle swarm algorithm and ant colony algorithm. Firstly, robot motion space model of the algorithm is created using link figure. After got fixed circulation rapid global, search to get more optimal path by means of improved fastest convergence ant system, then using a particle ants information communication method to update pheromone, finally, optimal path is drew. The simulation experiment shows that, even in the complex environment, this algorithm can also has the advantage of ant colony algorithm to optimize the result accurately and particle swarm algorithm local optimization accurately and rapidly, and a global security obstacle avoidance of optimal path is plot, the route is shorten 8% compare than the general ant colony algorithm.

Download Full-text