Improved Ant Colony Optimization algorithm by path crossover for optimal path planning

2009 ◽  
Author(s):  
Joon-Woo Lee ◽  
Jeong-Jung Kim ◽  
Ju-Jang Lee
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Optimal Path Planning

Solutions to VRP in Home Delivery Based on Ant Colony Optimization Algorithm

2010 ◽  
Vol 159 ◽  
pp. 100-104
Author(s):  
Meng Jie Hu ◽  
Jian Ping Wang ◽  
Xiao Min Li
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Home Delivery ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Optimal Path Planning ◽  
Service Policies ◽  
Planning Problems

Home delivery is a new trend in logistics at present. The distribution path planning has a great impact on customer’s satisfaction and the total cost of operation in home delivery industry. In This paper, we construct the distribution path planning problems in the industry of logistics and home delivery based on Ant Colony Optimization Algorithm, the optimal vehicle’s number and the best distribution path can be found in the shortest time by using the model advised in the paper. It is found that there is no obvious correlation between the service and the total costs of delivery after the analysis. So, home delivery companies can select the optimal path planning, i.e. a lower cost of delivery and higher level of service, according to their service policies.

scholarly journals A Novel Parallel Ant Colony Optimization Algorithm for Warehouse Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junqi Yu ◽  
Ruolin Li ◽  
Zengxi Feng ◽  
Anjun Zhao ◽  
Zirui Yu ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Automated Guided Vehicles ◽  
Stability And Convergence ◽  
Ant Colony Optimization Algorithm ◽  
Processing Efficiency ◽  
Minimum Number ◽  
The Stability

In order to improve the working efficiency of automated guided vehicles (AGVs) and the processing efficiency of fulfilling orders in intelligent warehouses, a novel parallel ant colony optimization algorithm for warehouse path planning is proposed. Through the interaction of pheromones among multiple subcolonies, the coevolution of multiple subcolonies is realized and the operational capability of the algorithm is improved. Then, a multiobjective function with the object of the shortest path and the minimum number of turns of the AGV is established. And the path satisfying this objective function is obtained by the proposed algorithm. In addition, the path is further smoothed by reducing the number of intermediate nodes. The results show that the stability and convergence rate of the algorithm are faster and more stable, compared to other algorithms, in generating paths for different complexity maps. The smoothing treatment of the path significantly reduces the number of turns and the path length in the AGV driving process.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

Sign in / Sign up

Export Citation Format

Share Document