Logistics Distribution Route Optimization Algorithm Based on Improved Ant Colony Algorithm of Gray Wolf Optimizer

2021 ◽  
Vol 11 (04) ◽  
pp. 892-901
Author(s):  
子程 周
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Gray Wolf ◽  
Logistics Distribution ◽  
Gray Wolf Optimizer ◽  
Improved Ant Colony Algorithm

The Research of Emergency Logistics Routing Optimization Based on Improved Ant Colony Optimization

2012 ◽  
Vol 482-484 ◽  
pp. 2519-2523
Author(s):  
Teng Fei ◽  
Li Yi Zhang ◽  
Yun Shan Sun ◽  
Hong Wei Ren
Keyword(s):  
Mathematical Model ◽  
Ant Colony Algorithm ◽  
Model Solution ◽  
Ant Colony ◽  
Traffic Situation ◽  
Logistics System ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Emergency Logistics ◽  
Improved Ant Colony Algorithm

Emergency logistics system contains information on material reserves, emergency command and emergency distribution. In this paper, the aspect of emergency distribution only is analyzed in microscopic, mathematical model of emergency logistics distribution has been established in considering the traffic situation and shortage degree. On the aspect of model solution, improved ant colony algorithm, which can enhance the selectivity of finding the best solution in emergency logistics distribution routing, is used in solving the model.

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1761-1764
Author(s):  
Feng Kai Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Route Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Logistics Distribution ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

Application of Emergency Logistics Distribution Routing Optimization Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1726-1732 ◽  
Author(s):  
Li Yi Zhang ◽  
Teng Fei ◽  
Jin Zhang ◽  
Jie Li
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Delivery Time ◽  
Emergency Relief ◽  
Logistics Distribution ◽  
The Road ◽  
Routing Optimization ◽  
Emergency Logistics ◽  
Improved Ant Colony Algorithm

Emergency relief has characteristics of complexity, urgency, sustainability, technicality, and so on. In this paper a mathematical model to seek the shortest delivery time as the ultimate goal is established based on these characteristics, which is on the core of characteristics with the urgency and consider both the road conditions and on shortage of demand point of relief supplies. The problem of emergency logistics distribution routing optimization is solved by the improved ant colony algorithm—Fish-Swarm Ant Colony Optimization (FSACO), simulation results show that, compared with basic ant colony algorithm, Fish-Swarm Ant Colony Optimization can find the higher quality to solve the problem of emergency logistics distribution routing optimization.

scholarly journals Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Zhang ◽  
Weibo Sun ◽  
Sang-Bing Tsai
Keyword(s):  
Partial Order ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Order Relation ◽  
Ant Colony ◽  
Route Optimization ◽  
Sports Facilities ◽  
Routing Method ◽  
Evacuation Plan ◽  
Evacuation Route

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

scholarly journals Study on the Route Optimization of Military Logistics Distribution in Wartime Based on the Ant Colony Algorithm

2010 ◽  
Vol 3 (1) ◽  
Author(s):  
Hongtao Shi ◽  
Yucai Dong ◽  
Lianghai Yi ◽  
Dongyun Zheng ◽  
Hong Ju ◽  
...  
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Military Logistics ◽  
Logistics Distribution
Sign in / Sign up

Export Citation Format

Share Document