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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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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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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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ant colony optimization algorithm with random perturbation behavior to the problem of optimal unit commitment with probabilistic spinning reserve determination

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ELECTRIC POWER SYSTEMS RESEARCH 2004年第2-3期69卷 295-303页

作者： Shi, LB Hao, J Zhou, JQ Xu, GY Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200030 Peoples R China Chongqing Univ Coll Elect Engn Chongqing 400044 Peoples R China

In this paper, a novel ant colony optimization algorithm with random perturbation behavior (RPACO) based on combination of general ant colony optimization and stochastic mechanism is developed for the solution of optimal unit commitment (UC) with probabilistic spinning reserve determination. In general, the purpose of UC is to enhance the economical efficiency as could as possible while simultaneously satisfying physical and operation constraints of individual unit. Consider the possibility of generating unit failure, the requirement, the sufficient spinning reserve capacity to ensure adequate reliability levels, must be satisfied by the commitment schedule. The security function approach is applied to evaluate the desired level of system security, and the proposed method in this paper, RPACO, is adopted to solve the UC problems. The effectiveness of the proposed method has been demonstrated on the corresponding numerical results. Further, the sensitivity of the desired security level to the optima during optimization is investigated in this paper. (C) 2003 Elsevier B.V. All rights reserved.

关键词： unit commitment reliability ant colony optimization algorithm transition strategy

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ant colony optimization algorithm applied to ship steering control 18th

Ant colony optimization algorithm applied to ship steering c...

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18th Annual International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Tomera, Miroslaw Gdynia Maritime Univ Dept Ship Automat PL-81225 Gdynia Poland

The article describes the application of an ant algorithm to optimize parameters of the ship course controller, based on the algorithm of PID control. The ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located. The procedure of parameter tuning for the ship course controller was applied to the case when the controller was changing the course of the ship and the integral action was turned off. Tuned parameters of the ship course controller are evaluated by the ant colony algorithm, which makes use of the course error based objective function and a given rudder deflection. The results were compared with equivalent results obtained using a genetic algorithm. Moreover, the effectiveness of PID controller parameter tuning was assessed using the ant colony optimization algorithm. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： ant colony optimization algorithm genetic algorithm artificial intelligence PID controller ship control

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ant colony optimization algorithm Based on Space Division for Container Loading Problem

Ant Colony Optimization Algorithm Based on Space Division fo...

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22nd Chinese Control and Decision Conference

作者： Wang, Li Zhang, Hui Xiong, Yan Li, Dawei Univ Sci & Technol Liaoning Sch Informat & Elect Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Sch Sci Anshan 114051 Peoples R China

ISBN: (纸本)9781424451821

This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. A mathematical model is given. Some principles which include space division, space merger, residual subspace omitted and loading rule are presented. A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading problem. The simulation results show that the model and the algorithm are effective.

关键词： Container Loading Problem ant colony optimization algorithm Space Utilization Space Division

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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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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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Process control using genetic algorithm and ant colony optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期26卷 501-516页

作者： Erguzel, Turker Tekin Akbay, Erbil Uskudar Univ Dept Comp Engn Istanbul Turkey Marmara Univ Tech Educ Fac Elect Comp Educ Dept Istanbul Turkey

Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an expert's experience.

关键词： Process control fuzzy controller ant colony optimization algorithm genetic algorithm artificial neural network

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Model Specification Searches in Structural Equation Modeling with a Hybrid ant colony optimization algorithm

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STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 2022年第5期29卷 655-666页

作者： Jing, Zeyuan Kuang, Huan Leite, Walter L. Marcoulides, Katerina M. Fisk, Charles L. Univ Florida Gainesville FL 32653 USA Univ Minnesota Minneapolis MN 55455 USA Univ Maryland College Pk MD 20742 USA

Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid ant colony optimization algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the ant colony optimization algorithm with the Tabu search algorithm to increase both accuracy and efficiency. A Monte Carlo simulation study showed that the hACO algorithm provided accurate and efficient SEM specification searches across all designed simulation conditions. The hACO algorithm can help applied researchers conduct specification searches while avoiding potential model misspecifications.

关键词： ant colony optimization algorithm data mining metaheuristic algorithm model misspecification specification search structural equation modeling Tabu Search

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