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ant colony optimization parallel algorithm and based on coarse-grained model

Jisuanji Gongcheng/Computer Engineering 2005年第1期31卷 157-159页

作者： Zhu, Qingbao Dept. of Computer Science Nanjing Normal Univ. Nanjing 210097 China

In order to improve the speed of convergence of ant colony optimization, a parallel algorithm based on coarse-grained model is proposed in the paper, search tasks are assigned to q ant subgroups, and parallel searching are finished by q subgroups. Results of experiment show that the algorithm described in this paper makes the searching speed hundreds of times faster than the latest improved algorithm.

关键词： ant colony optimization algorithm ant colony system Coarse-grained model Parallel algorithm

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The embedded feature selection method using ant colony optimization with structured sparsity norms

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COMPUTING 2025年第1期107卷 1-22页

作者： Nemati, Khadijeh Sheikhani, Amir Hosein Refahi Kordrostami, Sohrab Roudposhti, Kamrad Khoshhal Islamic Azad Univ Lahijan Branch Dept Appl Math & Comp Sci Lahijan Iran Islamic Azad Univ Lahijan Branch Dept Comp Engn Lahijan Iran

Feature selection is important in many machine learning applications. Our results demonstrate that it is not necessary to use all features of a dataset to perform classification and achieve lower classification error, which leads to higher accuracy. By selecting meaningful features and reducing the dimensions of the feature vector, we can significantly improve performance. The experimental results show that our proposed method, ant-ANN-SSN, consistently outperforms existing methods across various datasets. For example, with 200 features, ant-ANN-SSN achieved an accuracy of 77.85% on the RELATHE dataset and 77.68% on the PCMAC dataset (see Table 5). With 20 features, the ant-ANN-SSN method reached 97.89% accuracy for ALLAML and 96.89% for PROSTATE-GE (see Table 6). Our approach, which employs an ant colony optimization (ACO) algorithm alongside a two-layer perceptron classifier, addresses the feature selection problem as an optimization challenge, utilizing a new structured sparsity norm to evaluate feature subsets.

关键词： Feature selection Artificial neural network ant colony optimization algorithm

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Bidirectional artificial potential field-based ant colony optimization for robot path planning

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ROBOTICS AND AUTONOMOUS SYSTEMS 2025年 183卷

作者： Fu, Bo Chen, Yuming Quan, Yi Zhou, Xilin Li, Chaoshun Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

ant colony optimization (ACO) is a common approach for addressing mobile robot path planning problems. However, it still encounters some challenges including slow convergence speed, susceptibility to local optima, and a tendency to falling into traps. We propose a bidirectional artificial potential field-based ant colony optimization (BAPFACO) algorithm to solve these issues. First, the bidirectional artificial potential field is introduced to initialize the grid environment model and restrict direction selection to jump out of the trap. Second, an adaptive heuristic function is presented to strengthen directionality of the algorithm and reduce the turning times. Third, a pseudo-random state transition rule based on potential difference between starting and ending nodes is developed to accelerate convergence speed. Finally, an improved pheromone update strategy incorporating pheromone diffusion mechanism and elite ants update strategy is proposed to help getting out of local optima. To demonstrate the advantages of BAPFACO, the validation of the performance in six different complexity environments and comparative experiments with other conventional search algorithms and ACO variants are conducted. The results of experiment show that compared to various ACO variants, BAPFACO have advantages in terms of reducing the turning times, shortening path length, improving convergence speed and avoiding ant loss. In complex environments, compared to IHMACO, the average path length enhancement percentage (PLE) of BAPFACO is 20.98%, the average iterations enhancement percentage (IE) of BAPFACO is 20.00% and the average turning times enhancement percentage (TE) of BAPFACO is 49.43%. These results firmly demonstrate the efficiency and practicality of the BAPFACO algorithm for mobile robot in path planning.

关键词： Path planning ant colony optimization algorithm Bidirectional artificial potential field Pheromone update Heuristic function

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Trajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2012年第3期20卷 518-528页

作者： Unal, Muhammet Erdal, Hasan Topuz, Vedat Marmara Univ Dept Elect & Comp Educ Tech Educ Fac Istanbul Turkey Marmara Univ Vocat Sch Tech Sci Istanbul Turkey

The main goal of this study was to compare the performances of genetic algorithm (GA) and ant colony optimization (ACO) algorithm for PID controller tuning on a pressure control process. GA and ACO were used for tuning of the PID controller when predefined trajectory reference signal was applied. Offline learning approach was employed in both GA and ACO algorithms. Realized pressure process dynamic has nonlinear behavior, thus system was modeled by nonlinear auto regressive and exogenous input (NARX) type artificial neural network (ANN) approach. PID controller was also tuned by ZieglerNichols (ZN) method to compare the results. A cost function was design to minimize the error along the defined cubic trajectory for the GA-PID and ACO-PID controller. Then PID controller parameters (Kp, Ki, Kd) were found by GA-PID, ACO-PID algorithms, which were adjusted with their optimal parameters. It was concluded that both ACO and GA algorithms could be used to tune the PID controllers in the pressure process with excellent performance. This material is suitable for an engineering course on neural networks, genetic algorithm, ant colony optimization and process control laboratory. (c) 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 518528, 2012

关键词： pressure process ant colony optimization algorithm genetic algorithm PID controller artificial neural network

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Parameters optimization of Synchronous Induction Coilgun Based on ant colony algorithm

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2011年第1期39卷 100-104页

作者： Liu, Wenbiao Cao Yanjie Zhang, Yuan Wang, Jie Yang, Dawei Naval Aeronaut & Astronaut Univ Yantai 264001 Shandong Peoples R China

Electrical-to-kinetic energy conversion efficiency of synchronous induction coilguns (SICG) is the main limiting factor of its development. In the system of SICG, the change of any electromagnetic parameters can directly or indirectly affects the electrical-to-kinetic energy conversion efficiency. In order to improve the electrical-to-kinetic energy conversion efficiency of SICG, the electromechanical model of SICG was built at first in this paper. Then, the structural parameters of SICG with 60-mm caliber were optimized with ant colony optimization algorithm. The electrical-to-kinetic energy conversion efficiency of the system was regarded as the target function, while the center-to-center space between the drive coil and the armature, and the structural parameters of the drive coil and the armature were regarded as variables. Results of research indicated that the electrical-to-kinetic energy conversion efficiency of the system was improved through parameter optimization. In order to validate the results of parameter optimization, an experiment was carried out with the first stage of SICG.

关键词： ant colony optimization algorithm armature drive coil energy conversion efficiency efficiency of synchronous induction coilguns (SICG)

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Research on ant colony optimization network access algorithm based on model of vehicle fog calculation 2

Research on ant colony optimization network access algorithm...

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2nd International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Gan, Weimin Li, Juan Guo, Yan Guangzhou City Construct Vestibule Sch Dept Art & Design Guangzhou Peoples R China Hefei Thomas Sch High Sch Hefei Peoples R China

ISBN: (纸本)9781665427098

Today, the development of in-vehicle networks, the realization of intelligent vehicle connectivity, and the improvement of urban traffic safety, traffic congestion, energy and environmental protection, etc., are various social problems that we urgently need to solve. At present, the communication mechanism in terms of in-vehicle cloud service architecture and in-vehicle edge computing cannot meet the needs of business growth in the 5G era. This paper mainly studies the new optimization ideas based on the network access part of the vehicle fog calculation model. Based on the ant colony optimization algorithm, how to choose more effective nodes as the relay nodes of the network access part, so that the network resources can be effectively used, such as increasing the resource bandwidth available at the edge of the network is to avoid congestion problems as much as possible, which greatly relieves the pressure of computing and data processing in the cloud.

关键词： Vehicle networking Fog calculation Relay node ant colony optimization algorithm

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The multi-objective routing optimization of WSNs based on an improved ant colony algorithm

The multi-objective routing optimization of WSNs based on an...

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Xuwei Lizhi Si Chuan Univ Sch Elect & Informat Engn Chengdu Si Chuan Peoples R China

ISBN: (纸本)9781424437092

At present most of the WSNs routing protocol only have a single research objective. The overall network performance lacks of multi-objective inspection and evaluation. The basic task of multi-objective routing is to find a route in the network which has sufficient resources to optimize some network parameters and satisfy multiple constrains. A multi-objective model, constrained of bandwidth, is put forward, which optimizes function such as energy consumption, network delay and data packet lost rate. By adjusting the weight of each function, the algorithm adapts well to various services whose requirements for energy cost, delay and lost are different. As it is difficult to optimize multi-objective problem, an advanced ant colony algorithm based on cloud model (AACOCM) is proposed, which can effectively restrict the algorithm to falling in local best. Simulation results show that this novel method has a certain validity and feasibility and be able to adapt to different services requirements.

关键词： WSNs ant colony optimization algorithm Multi-object routing Cloud model

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Identification and Control optimization algorithm based on Neural Networks and ant colony

Identification and Control Optimization Algorithm based on N...

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International Conference on Computational Materials Science (CMS 2011)

作者： Yang, Jia Xu, Qiang Yu, ChengBo Chongqing Univ Technol Coll Elect Informat & Automat Chongqing 400050 Peoples R China Chongqing Univ Coll Comp Sci & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9783037851555

It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system's delay time was discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changed the input sample period of the neural network so that it could discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model prediction, searching PID controller parameters based on ant colony optimization algorithm, it was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.

关键词： Large Delay Time System Identification of Delay Time Prediction Model ant colony optimization algorithm

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HYBRID optimization OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ant colony algorithm WITH EMPHASIS ON BUILDING DETECTION 2

HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-R...

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ISPRS Int Joint Conf of 2nd Conf on Geospatial Information Research GI Research, 4th Conf on Sensors and Models in Photogrammetry and Remote Sensing (SMPR), 6th Conf on Earth Observation of Environmental Changes (EOEC)

作者： Tamimi, E. Ebadi, H. Kiani, A. KN Toosi Univ Technol Fac Geodesy & Geomat Engn Tehran Iran

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous ant colony optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

关键词： Hybrid optimization Object-based Classification ant colony optimization algorithm High Spatial Resolution Image

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An optimization Model for Evacuation Based on Cellular Automata and ant colony algorithm 7

An Optimization Model for Evacuation Based on Cellular Autom...

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7th International Symposium on Computational Intelligence and Design (ISCID)

作者： Ye, Zhiwei Yin, Yujie Zong, Xinlu Wang, Mingwei Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China

ISBN: (纸本)9781479970056

With the progress of construction technology, the modern buildings become more and more complicated and large-sized. It is difficult to avoid sudden and catastrophic emergency events for interior buildings. Hence, it is very important to establish an evacuation model to simulate evacuation behavior of pedestrians under real condition and make a scientific and effective evacuation plan. For handling with the problems of emergency evacuation path in buildings, an improved cellular automata model based on ant colony optimization algorithm (ACO) is proposed. The proposed model considers the neighbors moving rules of cellular automata and law of export selection rule is used as heuristic information of ant colony optimization algorithm, it reflects the behavior of personnel path choice more really. Finally, the model has been applied to simulate the whole evacuation process in a classroom. By simulating the process of pedestrian evacuation with the model, the results show that the proposed model could achieve shorter evacuation time than basic ACO, it's feasible for solving evacuation planning problem.

关键词： Cellular Automata Model Emergency Evacuation ant colony optimization algorithm

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