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An Intelligently Enhanced ant colony optimization algorithm for Global Path Planning of Mobile Robots in Engineering Applications

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SENSORS 2025年第5期25卷 1326-1326页

作者： Li, Peng Wei, Lei Wu, Dongsu Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210026 Peoples R China

Global path planning remains a critical challenge in mobile robots, with ant colony optimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this study proposes an intelligently enhanced ACO (IEACO) incorporating six innovative strategies. First, the early search efficiency is improved by implementing a non-uniform initial pheromone distribution. Second, the epsilon-greedy strategy is employed to adjust the state transition probability, thereby balancing exploration and exploitation. Third, adaptive dynamic adjustment of the exponents alpha and beta is realized, dynamically balancing the pheromone and heuristic function. Fourth, a multi-objective heuristic function considering both target distance and turning angle is constructed to enhance the quality of node selection. Fifth, a dynamic global pheromone update strategy is designed to prevent the algorithm from prematurely converging to local optima. Finally, by introducing multi-objective performance indicators, the path planning problem is transformed into a multi-objective optimization problem, enabling more comprehensive path optimization. Systematic simulations and experimentation were performed to validate the effectiveness of IEACO. The simulation results confirm the efficacy of each improvement in IEACO and demonstrate its performance advantages over other algorithms. The experimental results further highlight the practical value of IEACO in solving global path planning problems for mobile robots.

关键词： mobile robots ant colony optimization algorithm global path planning state transition probability

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An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

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OCEAN ENGINEERING 2024年 301卷

作者： Liang, Weixing Lou, Min Chen, Zhangxing Qin, Huiyang Zhang, Chen Cui, Chengwei Wang, Yangyang China Univ Petr East China Coll Petr Engn Dongying Shandong Peoples R China Univ Calgary Reservoir Simulat Grp Calgary AB Canada

Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional ant colony algorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced ant colony algorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.

关键词： Deep-sea mining Path planning ant colony optimization algorithm

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Application of ant colony optimization algorithm based on farthest point optimization and multi-objective strategy in robot path planning☆

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wu, Shuai Dong, Ani Li, Qingxia Wei, Wenhong Zhang, Yuhui Ye, Zijing Dongguan City Univ Sch Artificial Intelligence Dongguan 523419 Peoples R China Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

With the continuous development of high technology and the continuous progress of intelligent industry, mobile robots are gradually widely used in various fields. In the field of mobile robot research, path planning is crucial. However, the current ant colony optimization algorithm applied to mobile robot path planning still has some limitations, such as early blind search, slower convergence speed, and lower path smoothness. To overcome these problems, this paper proposes an ant colony optimization algorithm based on farthest point optimization and multi-objective strategy. The algorithm introduces new heuristic information such as the normal distribution model, triangle inequality principle, smoothness function, safety value function, etc. It adopts multi-objective comprehensive evaluation indexes to judge the quality of paths. For the high-quality and poor-quality paths, the algorithm takes additional pheromone increments and decrements in pheromone concentration to speed up the algorithm's convergence. Besides, the farthest point optimization strategy is used to improve the quality of the paths further. Finally, to verify the algorithm's effectiveness, the algorithm is compared with 20 existing methods for solving the robot path planning problem, and the experimental results show that the algorithm exhibits better results in terms of convergence, optimal path length, and smoothness. Specifically, the algorithm can produce the shortest path in four different environments while realizing the least number of turns with faster convergence, further proving the effectiveness of the improved algorithm in this paper.

关键词： ant colony optimization algorithm Path planning Normal distribution Multi-objective strategy Farthest point optimization

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A Multigranularity Learning Path Recommendation Framework Based on Knowledge Graph and Improved ant colony optimization algorithm for E-Learning

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第2期12卷 586-607页

作者： Zheng, Yaqian Wang, Deliang Xu, Yaping Li, Yanyan Beijing Normal Univ Fac Educ Sch Educ Technol Beijing 100875 Peoples R China Univ Hong Kong Fac Educ Hong Kong 999077 Peoples R China

In e-learning, extracting suitable learning objects (LOs) from a vast resource pool and organizing them into high-quality learning paths is crucial for helping e-learners achieve their goals. Numerous approaches have been proposed to recommend optimal learning paths for e-learners. However, it is essential to emphasize that e-learning systems typically consist of a wide range of LOs with varying levels of granularity, ranging from fine-grained to coarse-grained. Unfortunately, current research has not adequately considered the underlying granularity structure of LOs when optimizing learning paths. Existing methods primarily focus on organizing LOs at a single granularity level, limiting their applicability in real-world e-learning systems. To address the limitations, we propose a multigranularity learning path recommendation (MGLPR) framework that aims to flexibly and effectively integrate the diverse granularity levels of LOs into high-quality learning paths. In this framework, a two-layer [knowledge point (KP) and LO layers] model is developed to formulate the MGLPR problem as a constrained optimization problem and an improved ant colony optimization algorithm (IACO) is introduced to solve it to identify optimal learning paths for e-learners. To evaluate the effectiveness of the proposed IACO, we conducted extensive computational experiments using 30 simulation datasets with varying problem sizes and complexities. The results demonstrate that our proposed IACO achieves superior performance and robustness compared with other competitors. Additionally, an empirical study was conducted to investigate the efficacy of the proposed approach in an authentic learning context, with results indicating that the proposed method outperforms the traditional self-organized ones.

关键词： optimization Electronic learning Knowledge graphs Hybrid power systems Faces Complexity theory ant colony optimization Search problems Recommender systems Limiting ant colony optimization algorithm e-learning knowledge graph learning path recommendation (LPR) multigranularity

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Multi-Label Feature Selection Based on Improved ant colony optimization algorithm with Dynamic Redundancy and Label Dependence

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Computers, Materials & Continua 2024年第10期81卷 1157-1175页

作者： Ting Cai Chun Ye Zhiwei Ye Ziyuan Chen Mengqing Mei Haichao Zhang Wanfang Bai Peng Zhang School of Computer Hubei University of TechnologyWuhan430068China Xining Big Data Service Administration Xining810000China Wuhan Fiberhome Technical Services Co. Ltd.Wuhan430205China

The world produces vast quantities of high-dimensional multi-semantic ***,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and *** selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant *** ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement ***,existing methods do not consider crucial correlation information,such as dynamic redundancy and label *** tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label ***,the dynamic redundancy is assessed between the selected feature subset and potential ***,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label *** results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

关键词： Multi-label feature selection ant colony optimization algorithm dynamic redundancy high-dimensional data label correlation

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Improved ant colony optimization algorithm based on islands type for mobile robot path planning

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2024年第5期21卷

作者： Li, Qian Li, Qipeng Cui, Baoling Zhejiang Sci Tech Univ Key Lab Fluid Transmiss Technol Zhejiang Prov Hangzhou Peoples R China Zhejiang Univ Sci & Technol Sch Mech & Energy Engn 318 Liuhe Rd Hangzhou Zhejiang Peoples R China

According to the characteristics of ant colony optimization (ACO) algorithm in mobile robot path planning, such as local optimal solution, slow convergence speed, low search efficiency, and propensity to produce numerous deadlock ants, an improved ACO algorithm based on island type (insular ACO (INACO)) is introduced. In this algorithm, firstly, several islands are established between the starting and the ending position of environment, serving as intermediate nodes for the paths searched by the ants. This greatly reduces the number of deadlock ants. Additionally, rectangular areas are initialized with non-uniform pheromone levels between adjacent islands, while other areas are set to a constant minimum pheromone value. This prevents blind searches during the initial stages of path planning. Furthermore, an adaptive volatilization coefficient is introduced into the global pheromone update rules to balance the convergence and global search ability. Finally, optimal parameter combinations of INACO are determined by simulation. INACO algorithm is simulated in various grid maps and compared with other improved ACO algorithms. Results demonstrate its superior global optimal search capability and rapid convergence speed. The average iterative times is 2.7 in a 20 x 20 grid environment and 4.7 in a 30 x 30 grid environment. Notably, INACO produces very few lost ants, with mean values of 50.1 and 95.2 in 20 x 20 and 30 x 30 grid environments, respectively.

关键词： Path planning ant colony optimization algorithm deadlock ant non-uniform pheromone

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ant colony optimization algorithm in the Design of University Subject Teaching Platform 7

Ant Colony Optimization Algorithm in the Design of Universit...

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7th International Conference on Information Systems Engineering (ICISE)

作者： Fang, Guomin Qujing Med Coll Qujing Yunnan Peoples R China

ISBN: (纸本)9781450397889

The development of educational informatization has greatly changed the current educational model of colleges and universities (CAU). The use of informatization technology combined with the characteristics of disciplines to build a TP to promote the development of informatization teaching. This paper adopts the B/S model to design the framework of the university subject TP. The user only needs to install a browser to carry out teaching and learning activities, which simplifies the use of the system. With the support of Web technology, the teaching platform (TP) can not only realize teaching evaluation, but also manage and store subject resources. In the process of platform login, this paper uses the characteristics of ant colony optimization (ACO) algorithm to match users with password strings with high security factors, ensuring the system Login security.

关键词： ant colony optimization algorithm Subject Teaching Platform B/S Model Teaching Evaluation

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Development of Intelligent Learning Model Based on ant colony optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 317-327页

作者： Guo, Xiaojing Zhu, Xiaoying Liu, Lei Yantai Nanshan Univ Coll Technol & Data Yantai 265700 Peoples R China

In the process of the gradual popularization of online courses, learners are increasingly dissatisfied with the recommendation mechanism of imprecise courses when faced with a large number of course choices. How to better recommend relevant courses to targeted users has become a current research hotspot. An intelligent learning model based on ant colony optimization algorithm is introduced, which can accurately calculate the similarity between courses and learners. After structured classification, the model recommends courses to learners in the optimal way. The results showed that the accuracy of this method reached 10-20 when tested in Sphere and Ellipse functions, and the optimal solution for problem Ulysses21 was 27, which was better than Advanced Sorting ant System (ASrank), Maximum Minimum ant System (MMAS), and ant System (AS) based on optimization sorting. The proposed ant colony optimization algorithm had better convergence performance than ASrank, MMAS, and AS algorithms, with a shortest path of 53.5. After reaching Root Mean Square Error (RMSE) and Relative Deviation (RD) distributions of 6% and 8%, the stability of the proposed method no longer decreased with increasing RMSE. The accuracy did not vary significantly with changes in the dataset, and the reproducibility performance was better than other comparison models. In the scenarios of path Block and path Naive, the proposed algorithm had an average computation time of only 1011, which was better than the ant colony optimization (ACO) and Massive Multilingual Speech (MMS) models. Therefore, the proposed algorithm improves the performance of intelligent learning models, solves the problem of local optima while enhancing the convergence efficiency of the model, and provides new solutions and directions for increasing the recommendation performance of online learning platforms.

关键词： Online courses ant colony optimization algorithm intelligent learning model path planning local optimum

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optimization of ceramic process parameters supported by ant colony optimization algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Xia, Jingyang Wuhan Univ Sci & Technol Sch Art & Design 199 Xiongchu Ave Wuhan Hubei Peoples R China

Ceramic firing process parameters can directly affect mechanical properties and surface quality of ceramics. To improve the quality of ceramics, the ant colony optimization algorithm is applied. Parameters such as firing temperature, heating rate, and cooling rate are selected. Each ant represents a parameter combination. The initial pheromone is set to a constant. Simulation is carried out by random selection, and the fitness is calculated. Excellent combinations can accumulate more information, which makes subsequent ants tend to choose them and gradually converge to the optimal combination. When the fitness function value is stable, the algorithm terminates and outputs the optimal parameters. Whale optimization algorithm, Bayesian optimization algorithm, and genetic algorithm are used for comparison in the experiment. The results show that the ant colony optimization algorithm is more efficient in finding the optimal solution, reducing the number of iterations by 9% to 23% and saving 5% to 13% in time cost. The optimal solution of the ant colony optimization algorithm is used for firing, with the highest and most stable yield rate reaching 100%. The ant colony optimization algorithm can effectively optimize process parameters, improve ceramic quality, and reduce defects.

关键词： ceramic process parameter ant colony optimization algorithm parameter selection weight factor method fitness function

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A Differential Pheromone Grouping ant colony optimization algorithm for the 1-D Bin Packing Problem

A Differential Pheromone Grouping Ant Colony Optimization Al...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ali, Aseel Ismael Keedwell, Edward Helal, Ayah Univ Exeter Exeter Devon England

ISBN: (纸本)9798400704949

The bin packing problem (BPP) is a well-researched and important NP-hard problem with many contemporary applications (e.g. stock cutting, machine scheduling), which requires a set of items with variable sizes to be packed into a set of fixed-capacity containers. Many metaheuristic approaches have been successfully trialled on this problem, including evolutionary algorithms, ant colony optimization and local search techniques. The most successful variants of these approaches use grouping techniques whereby the algorithm considers sets of items together rather than as separate decision variables. This paper presents an ant colony optimization integrated with a grouping technique and a novel differential pheromone procedure for bin packing. The proposed differential pheromone grouping ACO shows state-of-the-art results for ACO approaches in BPP and approaches the performance of the best evolutionary methods.

关键词： ant colony optimization algorithm One-dimensional bin packing Grouping problem Differential pheromone

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