Heuristics based ant colony optimization for vehicle routing problem

Chen, Ruey-Maw; Hsieh, Fu-Ren; Wu, Di-Shiun

doi:10.1109/iciea.2012.6360876

Cited by 11 publications

(3 citation statements)

References 16 publications

(15 reference statements)

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“…Then, ACO is the main method to apply the mission of path searching, APF is to smooth the path and rise the ability of artificial ants [15] . Heuristics based ACO is a method to use semi-greedy heuristic and clustering phase transition rule to prove the searching ability [16] . What's more, Parallel ACO uses parallelized ants ACO and swarm intelligence technique to let method fit for any complex task [17] .…”

Section: Related Workmentioning

confidence: 99%

The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

Zhao¹,

Yan²,

Ye³

2018

Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

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Path planning in complex environment is the main foundation of flights automation navigation. Therefore, this paper aims to propose an algorithm combined ant colony optimal algorithm with potential field heuristic information. The main implement method of this algorithm is to use the information between the environment and goal to build the heuristic elements. What's more, the method defines this information as initial pheromone and converses elements to guide the ants to find the shortest path. Thanks to the heuristic elements in algorithm，the PFACO algorithm has a better searching tendency than traditional ACO algorithm，and overcome disadvantage in ants blindness. Through the simulation, the results show that PFACO has a better tendency of convergence. Compared with ACO, the PFACO algorithm can find a shorter path in same convergence time.

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Section: Related Workmentioning

confidence: 99%

The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

Zhao¹,

Yan²,

Ye³

2018

Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

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“…Finally, genetic algorithm was used to solve the problem. Other studies include [8][9][10][11][12][13][14] et al However, the above-mentioned literature mainly focuses on the analysis and research of single objective vehicle routing problem, and less research on multi-objective planning vehicle routing problem. Literature considering multiple-objective are no detailed analysis on the core issues such as target selection.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective vehicle routing problem with time windows based on improved simulated annealing algorithm

Huang²,

Shen³

2022

Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022)

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Aiming at the vehicle routing problem with time window constraints, this paper discusses the vehicle routing optimization problem among multiple warehouses, multiple products and multiple customers. In this paper, a multi-objective model of vehicle path optimization based on the improved simulated annealing algorithm is established by adding memory function and setting monotonous heating up and other real-time optimization strategies. The optimization objectives include total transportation cost, total driving distance and total driving time. In order to prove the effectiveness of the proposed model, the optimization results of standard genetic algorithm, random search and the algorithm in this paper are compared on the randomly generated dataset. The simulation results show that the algorithm designed in this paper is fast and efficient in all four cases.

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“…Hongbin et al 14 proposed a hybrid algorithm combining an improved A-Star algorithm and a dynamic window method, which has good performance in complex dynamic environment. Chen et al 15 defined the valuation function as an exponential decay method to reduce redundant expansion. Wang et al 16 proposed an efficient full area coverage path planning algorithm based on grid mapping.…”

Section: Introductionmentioning

confidence: 99%

Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

Zhang

2021

International Journal of Advanced Robotic Systems

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The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control.

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Heuristics based ant colony optimization for vehicle routing problem

Cited by 11 publications

References 16 publications

The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

Multi-objective vehicle routing problem with time windows based on improved simulated annealing algorithm

Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

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