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Multiple-Junction-Based Traffic-Aware Routing Protocol Using aco algorithm in Urban Vehicular Networks

SENSORS 2024年第9期24卷 2913页

作者： Lee, Seung-Won Heo, Kyung-Soo Kim, Min-A Kim, Do-Kyoung Choi, Hoon LIG Nex1 Dept C4I Res Seongnam 13488 South Korea Chungnam Natl Univ Dept Comp Sci & Engn Daejeon 34134 South Korea

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (aco) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average.

关键词： VANET VANET routing protocol traffic-aware aco algorithm multiple junction

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Optimization of the path planning model through integration of improved RRT∗algorithm and aco algorithm 6

Optimization of the path planning model through integration ...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Zhao, Xiyao Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350305623

Path planning is an important part of AUV (Autonomous Underwater Vehicle) exploration system. The path planning of AUV should consider the optimization goal of the path, the path environment, the terrain and obstacles, and the performance constraints of the robot itself. This path planning problem can be solved by various algorithms, but the commonly used algorithms often have many problems. Therefore, aiming at the problems of long sampling time, large amount of calculation and too random path planning results of RRT∗ algorithm, a multi-strategy improved NTDRRT∗ algorithm is proposed. Aiming at the problem that aco algorithm is easy to fall into local optimum and slow convergence speed, a multi-strategy improved HPaco algorithm is proposed. The NTDRRT∗ algorithm and the HPaco algorithm are combined to form a hybrid improved algorithm, and an AUV path planning model is proposed based on this. Through experimental verification, the hybrid path planning algorithm proposed in this paper has achieved good results in search efficiency, global convergence and solution quality. The research content and results of this paper have certain reference value for improving the efficiency and accuracy of AUV path planning, and have made certain contributions to the development of AUV technology, and can also provide reference and inspiration for research in related fields. © 2023 IEEE.

关键词： aco algorithm algorithm Path planning RRT

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A novel integrating between tool path optimization using an aco algorithm and interpreter for open architecture CNC system

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 178卷 114988-114988页

作者： Hatem, Noor Yusof, Yusri Kadir, Aini Zuhra A. Latif, Kamran Mohammed, M. A. Univ Tun Hussein Onn Malaysia Fac Mech & Mfg Engn Parit Raja Malaysia Univ Technol Malaysia Fac Mech Engn Skudai Malaysia Univ Basrah Coll Engn Dept Petr Engn Basrah Iraq Univ Tekn Malaysia Melaka UTeM Durian Tunggal Malaysia Univ Basrah Coll Engn Dept Mat Engn Basrah Iraq

This article proposes an effective methodology to reduce manufacturing time through the creation of quasi-optimal G command sequences, based on initial machining codes derived from CAD/CAM Service-Oriented Architecture (SOA) software. The optimization steps involved minimizing travel path time for CNC manufacturing machine milling and drilling. Also, identifying the optimal order of operation that enabled the shortest travel path for the cutting tool. These steps resulted in consistent enhancements of approximately 10.41% and 16.58% for milling and drilling machining, respectively. Furthermore, high-performing Ant Colony Optimization (aco) algorithms were used to optimize the travel path time. In the context of automatic NC program production, the optimization of the cutting tool's travel path could be achieved by integrating the aco algorithm and the Open Architecture Control (OAC) into commercial CAD/CAM. In this paper, a new optimized generation of CNC systems, based on combining the Open Architecture Control (OAC) technology supported by the G-code data model and the optimization algorithm are presented.

关键词： Milling Drilling G code aco algorithm Tool path optimization Traveling salesman problem

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A Noble Approach of aco algorithm for WSN 7

A Noble Approach of ACO Algorithm for WSN

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7th International Conference on Computer and Communication Engineering (ICCCE)

作者： Sharmin, Afsah Anwar, F. Motakabber, S. M. A. Int Islamic Univ Malaysia Dept Elect & Comp Engn Kuala Lumpur Malaysia

ISBN: (纸本)9781538669921

In energy compelled wireless sensor networks (WSNs), the means by which to perform effectual routing is among the main focuses. A noble approach of ant colony optimization (aco) algorithm for discovering the optimum route in the WSNs for data transmission is proposed here for enhancement and optimization considering the issue of path selection to reach the nodes. Using the proposed aco algorithm and considering both the node mobility and the existing energy of the nodes, an optimum route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified utilizing MATLAB and the simulation results demonstrate that new ant colony optimization based algorithm can achieve better performance and faster convergence to determine the best cost route.

关键词： Network Routing WSN aco algorithm IoT PSO ABC

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A Power-aware aco algorithm for the Cloud Computing Platform 18

A Power-aware ACO Algorithm for the Cloud Computing Platform

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4th International Conference on Communication and Information Processing (ICCIP)

作者： Yan, Wuyang Chen, Jianxin Li, Lianwan Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Minist Educ Nanjing Peoples R China

ISBN: (纸本)9781450365345

Cloud computing provides a variety of services. However, the data center for cloud computing consumes a lot of power. Reducing the power consumption of data center is very important. In this paper, we propose a Power-aware aco algorithm for the cloud computing platform, which optimizes the aco algorithm with the goal of minimizing power consumption. In order to solve the problem of too many hosts for migration that the classic aco cannot solve, we treat the idle hosts and active hosts differently. Together with the initialization based on the modified Power-aware Best Fit Decreasing algorithm, our Power-aware aco can handle VM migration problems no matter there are too many hosts or not. The simulation results show that our Power-aware aco is able to reduce 20% and 91% power consumption compared with VMP-aco in two different circumstances. In addition, our algorithm is also proved to be stable and reliable after different tests with various initial conditions.

关键词： Cloud computing Power consumption VM migration aco algorithm

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Solution of the inverse problem in solidification of binary alloy by applying the aco algorithm

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2016年第5期24卷 889-900页

作者： Hetmaniok, Edyta Univ Technol Inst Math Silesian Gliwice Poland

The paper presents a solution of the inverse problem consisting in reconstruction of the heat flux and the distribution of temperature in the process of binary alloy solidification when the temperature measurements in the selected points of the alloy are known. The considered task is mathematically modelled by means of the heat conduction equation with the substitute thermal capacity and with the liquidus and solidus temperatures varying in dependence on the concentration of the alloy component, whereas for describing the concentration the lever arm model is applied. An important part of the procedure consists in minimization of some functional executed with the aid of aco algorithm.

关键词： Solidification of binary alloy artificial intelligence swarm intelligence aco algorithm lever arm model

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The Method of Structural Damage Identification by aco algorithm and Wavelet Theory

The Method of Structural Damage Identification by ACO Algori...

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2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Deqing Guan Jun Tang Tao He School of Civil Engineering and Architecture Changsha University of Science and Technology

ISBN: (纸本)9781510835368

In order to effectively identify the location and extent of structural damage,a two-stage damage identification method,aco algorithm and wavelet theory were ***,the curvature mode of structure with damage was obtained with finite element *** curvature modes were analyzed using continuous wavelet transform by Mexh wavelet on multiple scales,and the location of the damage could be identified by the maximum of the wavelet ***,the objective function was established by the frequency and modal *** algorithm was used to solve constrained optimization problems,to identify the degree of structure *** the analysis to a three-span continuous beam,the results show that the aco algorithm and wavelet theory can effectively identify the damage location and damage degree of the structure.

关键词： wavelet aco algorithm damage identification finite element method

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An aco-based algorithm for Efficient XACML Policy Evaluation

An ACO-based Algorithm for Efficient XACML Policy Evaluation

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Yunpeng Zhang Beibei Zhang Department of Information and Logistics Technology University of Houston College of Software and Microelectronics Northwestern Polytechnical University

ISBN: (纸本)9781510845541

With the explosive growth of the internet, XACML policies have grown rapidly in size and complexity, and the efficiency of ABAC decision-making is unable to meet people's increasing demands, so this paper serves to solve this problem by providing a model based on an aco algorithm. The model first divides the XACML policy into different classifications by using an aco algorithm, then searches for related policies by calculating the Euclidean Distance with the request attribute values and XACML policy center attribute values. This approach transforms the policy evaluation into a numerical calculation. To evaluate the efficiency and the effectiveness of these methods, the paper conducts two sets of tests. The first results shows that the classification effect of aco algorithms is better than K-means;and the second results shows that the Euclidean Distance method is more efficient than the execution vectors used by Said Marouf, et al. to search for related policies.

关键词： XACML efficient policy evaluation aco algorithm euclidean distance method ABAC

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Ant colony optimisation and fuzzy system for prediction of computational data of fluid flow in a bubble column reactor

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2025年第1期37卷 95-109页

作者： Zandiyeh, Amirali Behroyan, Iman Noori, Mohammad Mahdi Babanezhad, Meisam Univ Tehran Coll Engn Sch Chem Engn Tehran Iran Shahid Beheshti Univ Fac Mech & Energy Engn Tehran Iran Iranian Inst Res & Dev Chem Ind Dept Catalysts & Absorbents Karaj Iran Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang Vietnam Shunderman Ind Strategy Co Dept Artificial Intelligence Tehran Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Computational data of a complex problem could be mapped and solved by artificial intelligence (AI) for postprocessing purposes and numerical time-saving. A fuzzy adaptive network as a hybrid AI system has already been used for CFD data machine learning. However, no investigation has been done for the ant colony optimisation (aco) performance evaluation compared to the adaptive network. As a state-of-the-art method, the aco and fuzzy system are combined and tuned to estimate the flow in a reactor. The liquid-phase velocity in the y-direction is predicted using a combination of fuzzy system and aco in a large domain. The findings further indicate that, in addition to the CFD dataset and Navier-Stokes's equations, employing an ant colony optimisation structure as a training technique may give an accurate approximation of two-phase flow. Furthermore, for numerical methods, a continuous link between input and output datasets may be a significant data source for the AI mechanism to train the physical process, incredibly bubbly flow. The ant colony optimisation shows a more accurate prediction than the adaptive network. As far as authors know, there are no such investigations to develop a new AI algorithm and performance analysis for fluid dynamics problems.

关键词： aco algorithm fuzzy intelligence pheromone influence CFD two-phase flow

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Combinatorial metaheuristic methods to optimize the scheduling of scientific workflows in green DVFS-enabled edge-cloud computing

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ALEXANDRIA ENGINEERING JOURNAL 2024年 86卷 458-470页

作者： Khaleel, Mustafa Ibrahim Safran, Mejdl Alfarhood, Sultan Gupta, Deepak Univ Sulaimani Dept Comp Coll Sci Sulaimani 46001 Kurdistan Regio Iraq King Saud Univ Coll Comp & Informat Sci Dept Comp Sci POB 51178 Riyadh 11543 Saudi Arabia Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India

A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While cloud computing offers advantages, it introduces scheduling delays caused by data transmission. To address this issue, edge computing has emerged as an alternative to traditional cloud systems, aiming to minimize latency. While various methods have been proposed to address this challenge, they often prioritize one aspect at the expense of overall system performance. In this paper, we present a novel algorithm utilizing ant colony optimization to compute a fitness function and prioritize multiple objectives in scheduling. The algorithm effectively determines how to distribute applications between edge and cloud servers to enhance computational efficiency. This entails a delicate balance between scheduling delays and energy consumption in two distinct phases. Initially, the algorithm identifies applications sensitive to delays and ensures their execution on local edge servers. Subsequently, it identifies applications that require intensive computation and migrates them to the cloud layer, where cloud servers can process them. The results demonstrate that this approach reduces delay costs by 21.19% and decreases energy consumption by 13.76%.

关键词： Application scheduling Cloud computing aco algorithm Edge computing Application placement

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