Open AccessEvacuation Entropy Path Planning Model Based on Hybrid Ant Colony-Artificial Fish Swarm Algorithms
Author(s) -
Fang Wang,
Jian Wang,
Xiaowei Chen
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/5/052025
Subject(s) - swarm behaviour , ant colony optimization algorithms , artificial bee colony algorithm , computer science , algorithm , mathematical optimization , convergence (economics) , entropy (arrow of time) , ant colony , path (computing) , artificial intelligence , mathematics , physics , quantum mechanics , economics , programming language , economic growth
The artificial fish swarm algorithm is introduced to construct the evacuation entropy path planning model of hybrid ant colony-artificial fish swarm algorithm to improve the convergence speed of the model. In the early stage of the model, the advantage of artificial fish swarm algorithm is used to search the optimal solution quickly and generate the initial solution. In the later stage of the model, the strong positive feedback mechanism of ant colony algorithm is used to quickly iterate out the optimal path. At the same time, the crowding factor of artificial fish swarm algorithm is introduced to update local pheromones adaptively combined with evacuation entropy. The simulation results show that the hybrid ant colony-artificial fish swarm algorithm has fast convergence speed and can avoid falling into local optimum.