Study of delivery path optimization solution based on improved ant colony model

Huang, Ruixiao; Ning, Jingyuan; Mei, Zhenghao; Fang, Xudong; Yi, Xiaomei; Gao, Yuanyuan; Hui, Guohua

doi:10.1007/s11042-021-11142-1

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…After all ants complete a cycle, they need to update the residual pheromone on the path, that is, the volatilization of pheromone. The pheromone on each path is adjusted according to formula (21):…”

Section: )Ant Colony Algorithm Based On Response Threshold Modelmentioning

confidence: 99%

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Optimization of Maritime Search and Rescue Resources Selection Based on Response Threshold Ant Colony Algorithm

Jiahui

Yang

et al. 2022

Frontiers in Artificial Intelligence and Applications

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The timeliness and severity of maritime search and rescue (MSAR) determine that the resource selection should be fast and optimized as much as possible. At present, there is no intelligent optimization method to assist decision-making, which is suitable for the actual characteristics of MSAR. In this paper, the optimization problem of MSAR resource selection is abstracted into a multi-objective optimization problem. Considering the needs of actual MSAR resources, the response threshold model is introduced to improve the ant colony algorithm. It can effectively solve the situation that the ordinary optimization algorithm is easy to fall into the local optimal solution and ignore the better resources. The research work of this paper is divided into three parts: model construction, model solution and model verification. Firstly, a multi-objective optimization model with five practical constraints is constructed to minimize the search time and maximize the average utility of resources. Then, the response threshold model considering the actual resource demand is used as the heuristic information in the ant colony algorithm. The stimulus model and threshold model in the response threshold model represent the ability of resources to perform tasks and the threshold of resources to perform tasks respectively, so as to improve the efficiency and speed of the algorithm. Finally, the algorithm is verified and compared through an example. The experimental results show that the average resource utility of this algorithm is 20.6% higher than that of the basic ant colony algorithm, and the MSAR success rate is also improved, which verifies the effectiveness of this method.

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Section: )Ant Colony Algorithm Based On Response Threshold Modelmentioning

confidence: 99%

“…It has strong robustness and is easy to be combined with other optimization algorithms. With the development of the algorithm, ant colony algorithm has been applied to many fields such as job scheduling and path planning [19][20][21][22]. However, the common ant colony algorithm does not better consider the resource requirements of MSAR.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Maritime Search and Rescue Resources Selection Based on Response Threshold Ant Colony Algorithm

Jiahui

Yang

et al. 2022

Frontiers in Artificial Intelligence and Applications

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“…ACO has shown good performance in searching the path on the graph, such as strong robustness, distributed computing, and ease of combination with other methods. Its positive information feedback mechanism enables it to find better solutions quickly [34]. However, when the flight environment is complex, the ant colony has a large search space when selecting the next region, and it takes a long time to provide positive feedback, resulting in poor real-time performance.…”

Section: Merge: Searching For a Set Of Sub-regionsmentioning

confidence: 99%

An Air Target Course Prediction Method Based on Sub-Regions Divide and Conquer With Double Variable Weight

Yang

Li³

2022

IEEE Access

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The number and density of airspace decompositions in course prediction are often fixed, resulting in large evaluation errors or low search efficiency of sub-regions, which makes it difficult to take into account the accuracy and real-time performance of course prediction. Aiming at this problem, a model based on the dynamic divide and conquer in sub-regions has been built. Firstly, a new airspace decomposition rule is proposed to adaptively decompose according to the complexity of the environment. Secondly, a state-distance bivariate weight function is constructed. The weights of course prediction index system are dynamically adjusted to improve the evaluation reliability of the selection possibilities of subregions. Then, to realize a comprehensive course prediction, the quality function of the course sequence is constructed from length, tortuosity, and selection possibility. Finally, a heuristic function based on possibility factor and distance factor and a pheromone update rule based on positive and negative feedback mechanism are constructed to improve the convergence speed and quality of course prediction. Compared with the airspace uniform decomposition strategy, course evaluation quality function, and ant colony algorithm experiment, the proposed method has a faster convergence speed and a higher sequence quality.

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