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15th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Othman, Muktar M. T. Adeshina, Steve A. Boukar, Moussa M. Nile Univ Nigeria Dept Elect & Elect Engn Abuja Nigeria Nile Univ Nigeria Dept Comp Engn Abuja Nigeria

ISBN: (纸本)9781728151601

This study aim is to design a road anomaly transmission algorithms using ant colony optimization (ACO) based Technique in a Vehicle-to-Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communication. The developed VACO also uses the features of VANET to find out the optimal path by considering a minimum number of nodes and cost parameters, which provides information related to accidents, speed of neighbouring vehicle and weather to help users in making informed decisions. Vehicle routing protocol based on ACO (VACO) also ensures to mitigate issues by combining the reactive and proactive approach and considers the parameters affecting the Quality of Service (QoS) such as latency, bandwidth, and delivery ratio in evaluating the algorithms.

关键词： Data Transmission ant colony optimization algorithm VANET

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FPGA-Based Path Planning Using Improved ant colony optimization algorithm 5

FPGA-Based Path Planning Using Improved Ant Colony Optimizat...

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5th IEEE International Conference on Consumer Electronics Berlin

作者： Hsu, Chen-Chien Hou, Ru-Yu Kao, Wen-Chung Li, Shih-An Natl Taiwan Normal Univ Dept Elect Engn Taipei Taiwan Tamkang Univ Dept Elect Engn New Taipei Taiwan

ISBN: (纸本)9781479987481

This paper develops a novel path planning algorithm using improved ant colony optimization (ACO) and its FPGA implementation. The proposed approach can effectively increase the accuracy to generate an optimal path. The main idea of this paper is to avoid local minimum by continuous tuning of a setting parameter and the establishment of new mechanisms for opposite pheromone updating and partial pheromone updating. Experimental results show that the execution efficiency of path planning is significantly improved by full hardware design for embedded applications.

关键词： Path Planning ant colony optimization algorithm FPGA

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Application of the hybrid algorithm combining ant colony optimization algorithm with microgenetic algorithm to the optimization of multilayered radar absorbing coating

Application of the hybrid algorithm combining ant colony opt...

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International Conference on Microwave and Millimeter Wave Technology

作者： Chao, Kun Liu, Yunlin Yang, Rugui SW Jiaotong Univ Inst Electromagnet Chengdu 610031 Peoples R China

ISBN: (纸本)9781424418794

A new optimization technique based on the hybrid algorithm combining ant colony optimization algorithm with microgenetic algorithm is presented for the design of multilayered radar absorbing materials. During the optimization procedure the optimization constrained conditions are different in order to meet the practical requirements in the different frequency bands between 2 GHz and 18 GHz, and the multilayered radar absorbing materials is also designed for a given maximum total thickness. The effects of the thickness and the number of layers on the optimization results are discussed in detail. The numerical results show that this new hybrid algorithm can obtain a better solution than that of the genetic algorithm.

关键词： multilayered radar absorbing materials hybrid algorithm ant colony optimization algorithm microgenetic algorithm

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Security Test Case Prioritization through ant colony optimization algorithm

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Computer Systems Science & Engineering 2023年第12期47卷 3165-3195页

作者： Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Mohd Waris Khan Mohd Faizan Alka Agrawal Dhirendra Pandey Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Computer Application Integral UniversityLucknowUttar Pradesh226026India Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknowUttar Pradesh226025India

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software *** of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test *** effectively applying test case prioritization,both the time and cost required for developing secure software can be *** paper proposes a test case prioritization technique based on the ant colony optimization(ACO)algorithm,a metaheuristic *** performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional *** has been applied to a Mobile Payment Wallet application to validate the proposed *** results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD *** ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering *** findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

关键词： Confidentiality integrity authentication non-repudiation resilience authorization ant colony optimization algorithm

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Identification and control of boiler combustion system based on neural networks and ant colony optimization algorithm

Identification and control of boiler combustion system based...

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7th World Congress on Intelligent Control and Automation

作者： Xu, Qiang Yang, Jia Yang, Yanqiu Chongqing Technol & Business Univ Coll Comp Sci & Informat Engn Chongqing 400067 Peoples R China Chongqing Univ Coll Automat Chongqing 400030 Peoples R China

ISBN: (纸本)9781424421138

Due to large delay time, varying coal's quality and steam load, boiler combustion system was difficulty controlled. Nonlinear system's delay time must be well identified. The abrupt mutation result from the training error sum square of the real output and the expected output of the neural network was used to identify the delay time. The input sample period of the neural network was changed so that it could discriminate the delay time of the nonlinear model. The discriminated large time-delay was applied to neural network prediction model. The errors between input and prediction model output were used to search PID controller parameters based on ant colony optimization algorithm. The method 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 neural network prediction model ant colony optimization algorithm

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ShortForm: An R Package to Select Scale Short Forms With the ant colony optimization algorithm

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APPLIED PSYCHOLOGICAL MEASUREMENT 2018年第6期42卷 516-517页

作者： Raborn, anthony W. Leite, Walter L. Univ Florida Gainesville FL USA

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A Combination Forecast Model of the Creative Talents Demand Based on ant colony optimization algorithm

A Combination Forecast Model of the Creative Talents Demand ...

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10th International Conference on Innovation and Management

作者： Li Woyuan Zhang Qingpu Qiao Jianmin Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Inner Mongolia Agr Univ Sch Math & Stat Hohhot 010018 Peoples R China

ISBN: (纸本)9787562942313

Creative industry has become a new economic growth point and the principal aspect of the competition of comprehensive national strength. Creative industry is a talent-intensive industry which stresses individual creativity and innovation. This paper discusses the definition and characteristics of creative talents of the creative industry, applies the combination forecast based on ant colony optimization algorithm to build a new creative talents demand forecasting model, and uses the statistical data of personnel in culture industry of Heilongjiang province to make empirical prediction. Empirical analysis indicates that the forecast model can improve the overall performance of the combined forecasts and meet the actual demand of the creative industry's development for creative talents.

关键词： Creative talents Combination forecast model ant colony optimization algorithm

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Design of Rural Ambient Intelligence Monitoring System Based on ant colony optimization algorithm 1

Design of Rural Ambient Intelligence Monitoring System Based...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Li, Shuai Ye, Jingyu Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350316469

The rural information monitoring system based on wireless sensor network (WSN) technology can be precisely used for monitoring the rural environment. In a data-centric WSN, to ensure accurate information transmission, channel equalization technology is needed to solve this problem. Optimizing routing protocols can balance network energy consumption and prevent important nodes from being consumed too early. Reasonable node deployment can not only reduce energy consumption but also promote network communication. Therefore, in order to improve the communication quality of WSN, this paper used ant colony optimization (ACO) algorithm to improve the communication quality of WSN, and designed a rural ambient intelligence monitoring system according to this algorithm. Finally, data reliability and communication quality tests were conducted on the intelligent monitoring system. The experimental results showed that the error between the air humidity collected by the rural ambient intelligence monitoring platform and the manually sampled air humidity was not more than 0.5%, the error of measuring the PM2.5 content in the air was not more than 0.5mcg, and the network packet loss rate was always below 5%. The results of many tests showed that the system runs stably and has high monitoring accuracy, which can meet the needs of rural ambient intelligence monitoring. © 2023 IEEE.

关键词： ant colony optimization algorithm Intelligent Detection Rural Environment Wireless Sensor Network

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Knowledge-driven ant colony optimization algorithm for vehicle routing problem in instant delivery peak period

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Hou, Ying Guo, Xinyu Han, Honggui Wang, Jingjing Beijing Univ Technol Fac Informat Technol Engn Res Ctr Digital Community Beijing Lab Urban Mass TransitMinist Educ Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China

Instant delivery is an important part of urban logistics distribution, which realizes point-to-point distribution between merchants and customers. During the peak period of orders, instant delivery is a large-scale variable NP-hard combinatorial optimization problem, which increases the difficulty and complexity of scheduling greatly. To solve the large-scale vehicle routing problem of instant delivery in peak periods, a knowledge-driven ant colony optimization (KDACO) algorithm is proposed in this paper. First, the knowledge base is established to guide evolutionary search, including the knowledge of order priority and the feature knowledge of feasible schemes. Second, the pheromone supplementation strategy is designed based on the knowledge of order priority, enhancing the guiding ability of the pheromone table. Third, the adaptive evolutionary operator is designed based on the feature knowledge of feasible schemes, improving the optimization efficiency of the algorithm. Finally, numerical experiments on extensive classical datasets show that the proposed KDACO can obtain superior performance to other state-of-the-art algorithms in the instant delivery peak period. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： ant colony optimization algorithm Vehicle routing problem Instant delivery Peak period

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A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm

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APPLIED SOFT COMPUTING 2020年 96卷 106621-106621页

作者： Khelifa, Boudjemaa Laouar, Mohamed Ridda Larbi Tebessi Univ Tebessa Lab Math Informat & Syst LAMIS Tebessa Algeria

Hitherto, urbanization reached unprecedented spreading, various problems in the field increase from day to day, and makes the urban phenomena more dynamic and more complex. Therefore, it is important to call in experts and provide all stuff to establish urban projects' plans, which often need to be achieved in a brief time. Actually, decision-makers need more and more updated plans and even sustainable solutions to convey eventual urban changes with maintaining intrinsic features of urban areas, such as coverage, inter-dependency, and coherency. Due to decision-makers yearnings and the short time allocated to planners, urban project planning remains an exhausting task;it resorts to arbitrary choices to find a good match of projects according to the intended situations. On the other hand, it should take care of the available resources like funds, land, water, energy, underground, and raw materials, which ought to be rationally exploited, and preserved for future generations. In this paper, the proposed intelligent decision support system (IDSS) aims to find out the best urban plans that fit urban projects to appropriate areas. It also employs the holonic approach to model complex and large-scale urban systems, where agents of each level apply a new multi-objective ant colony optimization algorithm called BKPACS for the urban project planning problem, which is viewed as a bounded knapsack problem (BKP). To produce global optimal urban plans, the main algorithm called H-MACO coordinates between the different levels of this holonic system. The experimental results on a set of urban projects about a province of Algeria show good quality plans produced in less time. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Intelligent decision support system Holonic multi-agent system Urban project planning ant colony optimization algorithm Bounded knapsack problem

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