Article
Multidisciplinary Sciences
Chuang Zhang, He Wang, Li-Hua Fu, Yue-Han Pei, Chun-Yang Lan, Hong-Yu Hou, Hua Song
Summary: Fruit-picking robots are important for promoting agricultural modernization and improving efficiency. By adopting the optimal sequential ant colony optimization algorithm (OSACO), which uses a continuous picking method, the robots can significantly improve their efficiency. The algorithm introduces innovative mechanisms to ensure global search capability and solve convergence problems. The results show that OSACO algorithm has better performance in terms of global search capability, convergence quality, path length, and robustness compared to other variants of the ant colony algorithm.
Article
Optics
Dan Liu, Xiulian Hu, Qi Jiang
Summary: This paper combines the improved ant colony algorithm to study the logistics distribution path design and optimization. The fusion of genetic algorithm and improved ant colony algorithm transforms the path optimal solution into the initial distribution of pheromone, and the mutation operator expands the search space and accelerates the convergence to the optimal solution. The logistics distribution route optimization design and optimization method based on the improved ant colony algorithm proposed in this paper has good results.
Article
Computer Science, Information Systems
Haifeng Ling, Yi Fu, Ming Hua, An Lu
Summary: The article investigates an intelligent approach to generate many-to-many vehicle-cargo matches, using clustering technology and ant colony optimization to maximize the platform's matching rate and profit. The proposed algorithm achieves accurate and stable recommendation results compared with other search methods, providing satisfactory solutions for vehicle drivers and cargo owners.
Article
Computer Science, Artificial Intelligence
Sidi Li, Tianyu Luo, Ling Wang, Lining Xing, Teng Ren
Summary: With the rapid development of tourism in the economy, there is an increasing demand for tourism. However, unreasonable distribution of resources in tourist attractions leads to problems such as decreased tourist satisfaction and income. This study proposes a mathematical model and algorithm that consider tourists' age, preferences, and carrying capacity of tourism routes to maximize overall tourist satisfaction and income. The improved ant colony algorithm shows higher efficiency in solving the tourism route planning problem and achieves favorable path optimization effects, confirming the effectiveness of the model.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Biotechnology & Applied Microbiology
Kangjing Shi, Li Huang, Du Jiang, Ying Sun, Xiliang Tong, Yuanming Xie, Zifan Fang
Summary: This article proposes an improved genetic and ant colony hybrid algorithm to address the problem of unsmooth path planning for intelligent vehicles. By improving the heuristic function in the ant colony optimization algorithm and making adaptations to the fitness function, crossover factor, mutation factor, and other aspects of the genetic algorithm, the improved hybrid algorithm achieves optimized new populations. Simulation and physical experiments demonstrate the effectiveness of the improved hybrid algorithm, reducing the average number of turns in simple and complex grids.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Xinsen Zhou, Wenyong Gui, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen
Summary: Continuous ant colony optimization algorithm incorporates a random following strategy to enhance global optimization performance and effectively handle high-dimensional feature selection problems. The algorithm performs competitively with other state-of-the-art algorithms in benchmark tests and outperforms well-known classification methods on high-dimensional datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Civil
Juan Cheng
Summary: Dynamic path optimization is crucial in intelligent transportation systems, and this study proposes an improved ant colony algorithm to achieve this goal. The influencing factors of the multiobjective planning model are determined through investigation and analysis. The algorithm is enhanced by using the analytic hierarchy process (AHP) to calculate the comprehensive weight of path length, travel time, and traffic flow. Directional guidance and dynamic optimization are also incorporated. The experimental results show that the improved ant colony algorithm outperforms the basic ant colony algorithm and the spatial shortest distance algorithm, providing more accurate and effective optimal paths.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
Article
Computer Science, Artificial Intelligence
Abdelrahman Elsaid, Karl Ricanek, Zimeng Lyu, Alexander Ororbia, Travis Desell
Summary: Continuous Ant-based Topology Search (CANTS) is a novel nature-inspired neural architecture search algorithm based on ant colony optimization. It utilizes a continuous search space to automate the design of artificial neural networks, removing the limitation of predetermined structure sizes. By adding an extra dimension for neural synaptic weights, CANTS can optimize both architecture and weights, significantly reducing optimization time while maintaining competitive performance.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Serap Ercan Comert, Harun Resit Yazgan
Summary: This paper introduces three multi-objective electric vehicle routing problems that consider different charging strategies and electric vehicle charger types while optimizing five conflicting objectives. A new hierarchical approach consisting of Hybrid Ant Colony Optimization (HACO) and Artificial Bee Colony Algorithm (ABCA) is developed to solve these problems. The proposed approach is examined on test-based instances and achieves the best new results in most cases.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Feng Wu
Summary: In the context of normalization of the epidemic, contactless delivery has become a major research area. This paper proposes a contactless distribution path optimization algorithm based on an improved ant colony algorithm, which analyzes traffic factors and customer satisfaction in the epidemic environment, and improves efficiency and user satisfaction. Through simulation optimization and comparative analysis, the effectiveness of the proposed model and algorithm is verified.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Geography, Physical
Qingyang Wang, Guoqing Zhou, Ruhao Song, Yongfan Xie, Mengyuan Luo, Tao Yue
Summary: This paper proposes a novel method for selecting a mosaic seamline network in orthophotos using a continuous space ant colony algorithm. Experiments demonstrate that the proposed method achieves better performance compared to existing commercial software and algorithms.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Mathematics
Shugang Li, Yanfang Wei, Xin Liu, He Zhu, Zhaoxu Yu
Summary: This study proposes a SEACO algorithm to improve the optimization speed of ant colony optimization (ACO) algorithm. Experiment results show that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, making it more suitable for large-scale data sets.
Article
Mathematics
YuLu Tang, Zamira Madina
Summary: An ant colony optimization algorithm-based flow spatial planning model for classical gardens has been designed in this study. By quantifying spatial scale and modifying spatial sequence, the spatial planning effect of classical gardens has been improved, which is of great popularization value.
JOURNAL OF MATHEMATICS
(2022)
Article
Multidisciplinary Sciences
Shengbin Liang, Tongtong Jiao, Wencai Du, Shenming Qu
Summary: The improved ant colony optimization algorithm combines contextual information of scenic spots with pheromone update strategy to optimize tourism route planning, resulting in routes that better cater to tourist preferences. The introduction of sub-path support degree helps prevent falling into local optima.
Article
Computer Science, Artificial Intelligence
Chao Liu, Lei Wu, Wensheng Xiao, Guangxin Li, Dengpan Xu, Jingjing Guo, Wentao Li
Summary: In this study, a novel variant of ant colony optimization algorithm called improved heuristic mechanism ACO (IHMACO) is proposed. It contains four improved mechanisms to enhance the efficiency and effectiveness of path planning. Experimental results show that IHMACO outperforms existing approaches in terms of path turn times.
KNOWLEDGE-BASED SYSTEMS
(2023)
