Gotowe bibliografie tematyczne / Secure Ant Colony Optimization

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Secure Ant Colony Optimization”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Secure Ant Colony Optimization"

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Maheshwari, Somesh. "Secure Route Selection in Manet Using Ant Colony Optimization." American Journal of Networks and Communications 4, no. 3 (2015): 54. http://dx.doi.org/10.11648/j.ajnc.s.2015040301.19.

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Wang, Xueli. "Low-Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm." Journal of Sensors 2021 (October 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/7633054.

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As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to
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Ameen, Nabeena, Najumnissa Jamal, and L. Arun Raj. "Comparative analysis of energy based optimized dynamic source multipath routing protocol in WSNs." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 441. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp441-455.

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<p>Wireless Sensor Network (WSN) has been considered to be a formula for overcoming various requirements like surveillance, health care, traffic surveillance and defense systems. As an aid to the demands for Wireless sensor network, research has been going on the efficient consumption of energy and secure communication areas for WSN. Here introducing Ant colony optimization based Optimized dynamic Secure multipath routing protocol (ODMRP) have improvised WSN in terms of energy efficiency, performance, strength and scalability. This paper proposed mechanism of Ant Colony Optimization base
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B, Vatchala, and G. Preethi. "Enhanced Ant Colony Optimization Based Secured Routing in Cloud Computing." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 231–39. https://doi.org/10.21275/sr21616115833.

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Panda, Niranjan, and Pankajini Panda. "A secure and energy efficient ant routing algorithm." Intelligent Decision Technologies 14, no. 3 (2020): 325–36. http://dx.doi.org/10.3233/idt-190042.

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In Mobile Ad hoc Networks (MANETs), setting up an ideal and proficient route linking the conveying bodies is the essential objective of the routing protocols. But any assault during the routing stage may upset the communication, paralyzing the whole network. So, providing security in routing for a protected communication between nodes has become a prime concern. In the present study, we propose a Secure Energy Efficient Ant Routing Algorithm (SEEARA) based on Ant Colony Optimization (ACO) algorithm and cryptographic primitives that exercises on power control and secure routing between a pair o
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Pande, Suyog Vinayak, K. Nattarkannan, M. Rama Bai, et al. "Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices." Computational Intelligence and Neuroscience 2022 (August 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/7934582.

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Logistics is the transfer of goods from one place to another, mostly from the production house to the customers. A logistics network is a set of operations that involve designing, production, and marketing the goods. Cold-chain logistics are those that needed to be transported in a cold refrigeration right from the production house to the customer. A secured networking model is essential to handle the logistics networks. In this article, we are going to see an intelligent secured networking model to identify the optimal path for cold-chain logistics to hospitals. The optimal pathfinder is used
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Hevin Rajesh, D., and B. Paramasivan. "Cluster based secure authentication technique using ant colony optimization in wireless sensor networks." Journal of Intelligent & Fuzzy Systems 31, no. 1 (2016): 423–32. http://dx.doi.org/10.3233/ifs-162155.

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Saleem, Kashif, and Iftikhar Ahmad. "Ant Colony Optimization ACO Based Autonomous Secure Routing Protocol for Mobile Surveillance Systems." Drones 6, no. 11 (2022): 351. http://dx.doi.org/10.3390/drones6110351.

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Sensing plays a vital role in enabling smart cities. The mobile surveillance of different sectors, the retransmission of radio signals, and package delivery are the main applications conducted by unmanned vehicles in smart cities. Multiple unmanned vehicles or miniaturized real-time flying machines with onboard sensors, whether land- or air-based, communicate with each other to form a flying sensor network. Almost all of these machines are battery-operated. Therefore, power preservation is an extremely important factor to be taken into consideration. This paper proposes a power-aware biologica
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Djalal, Muhammad Ruswandi, and Herman HR. "Speed Control Series Dc Motor Using Ant Colony Optimization." Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) 20, no. 2 (2019): 105. http://dx.doi.org/10.30595/techno.v20i2.4531.

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Motor DC yang terhubung dengan seri atau shunt banyak digunakan dalam berbagai aplikasi. Karena memiliki torsi yang relatif tinggi untuk memikul beban dibandingkan dengan motor permanen magnet dengan ukuran yang sama. Motor permanen magnet bersifat linear sedangkan motor DC bersifat non linear. Karakteristik non linear dari motor DC seperti saturasi dan gesekan dapat menurunkan kinerja dari konvensional Kontrol. Algoritma Ant Coloni pada prinsipnya mencari sumber makanan berdasarkan jejak feromone yang kemudian secara berkelompok akan mengikuti jejak yang memiliki feromone yang terbesar. Denga
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Sun, Ziwen, Min Wei, Zhiwei Zhang, and Gang Qu. "Secure Routing Protocol based on Multi-objective Ant-colony-optimization for wireless sensor networks." Applied Soft Computing 77 (April 2019): 366–75. http://dx.doi.org/10.1016/j.asoc.2019.01.034.

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Rozprawy doktorskie na temat "Secure Ant Colony Optimization"

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Feng, Yinda. "Ant colony for TSP." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4824.

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The aim of this work is to investigate Ant Colony Algorithm for the traveling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. This paper is based on the ideas of ant colony algorithm and analysis the main parameters of the ant colony algorithm. Experimental results for solving TSP problems with ant colony algorithm show great effectiveness.
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Angus, Daniel John. "Niching ant colony optimisation." Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/36804.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, Complex Intelligent Systems Laboratory, 2008.<br>Submitted in partial fulfilment for the degree of Doctor of Philosophy, Complex Intelligent Systems Laboratory, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2008. Typescript. Includes bibliographical references (p. 169.181).
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Krenzke, Tom (Tom Paul). "Ant colony optimization for agile motion planning." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35292.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 67-69).<br>With the need for greater autonomy in unmanned vehicles growing, design of algorithms for mission-level planning becomes essential. The general field of motion planning for unmanned vehicles falls into this category. Of particular interest is the case of operating in hostile environments with unk
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Pettersson, Lars, and Johansson Christoffer Lundell. "Ant Colony Optimization - Optimal Number of Ants." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229764.

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The focus of this thesis paper is to study the impact the number of ants has on the found solution of the Ant Colony Optimization (ACO) metaheuristic when solving the Traveling Salesman Problem. The goal was to find out how the length of the computed tours change for different amounts of ants within a limited number of iterations. To study this, three well known versions of the ACO algorithm were implemented and tested: Min-Max Ant System (MMAS), Elitist Ant System (EliteAS) and Ranked Ant System (RankedAS). The results showed trends that were consistent over several test cases. EliteAS and Ra
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Renfrew, David T. "TRAFFIC SIGNAL CONTROL WITH ANT COLONY OPTIMIZATION." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/190.

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Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users’ delays. Ant Colony Optimization (ACO) is a metaheuristic based on the behavior of ant colonies searching for food. ACO has successfully been used to solve many NP-hard combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic flow networks. This thesis investigates the application of ACO to minimize user delay at traffic intersections. Computer simulation results show that this new approach outperforms conventional fully actuated control under
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CHEN, Yijun. "Ant colony optimization approach for stacking configurations." Digital Commons @ Lingnan University, 2011. https://commons.ln.edu.hk/cds_etd/2.

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In data mining, classifiers are generated to predict the class labels of the instances. An ensemble is a decision making system which applies certain strategies to combine the predictions of different classifiers and generate a collective decision. Previous research has empirically and theoretically demonstrated that an ensemble classifier can be more accurate and stable than its component classifiers in most cases. Stacking is a well-known ensemble which adopts a two-level structure: the base-level classifiers to generate predictions and the meta-level classifier to make collective decisions.
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Kollin, Felix, and Adel Bavey. "Ant Colony Optimization Algorithms : Pheromone Techniques for TSP." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208374.

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Ant Colony Optimization (ACO) uses behaviour observed in real-life ant colonies in order to solve shortest path problems. Short paths are found with the use of pheromones, which allow ants to communicate indirectly. There are numerous pheromone distribution techniques for virtual ant systems and this thesis studies two of the most well known, Elitist and Max-Min. Implementations of Elitist and Max-Min ACO algorithms were tested using instances of the Traveling Salesman Problem (TSP). The performance of the different techniques are compared with respect to runtime, iterations and approximation
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Blum, Christian. "Theoretical and practical aspects of ant colony optimization." Doctoral thesis, Universite Libre de Bruxelles, 2004. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211187.

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Combinatorial optimization problems are of high academical as well as practical importance. Many instances of relevant combinatorial optimization problems are, due to their dimensions, intractable for complete methods such as branch and bound. Therefore, approximate algorithms such as metaheuristics received much attention in the past 20 years. Examples of metaheuristics are simulated annealing, tabu search, and evolutionary computation. One of the most recent metaheuristics is ant colony optimization (ACO), which was developed by Prof. M. Dorigo (who is the supervisor of this thesis) and coll
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Sharkey, Jeffrey Allen. "Automated radio network design using ant colony optimization." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/sharkey/SharkeyJ0508.pdf.

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Radio networks can provide reliable communication for rural intelligent transportation systems (ITS). Engineers manually design these radio networks by selecting tower locations and equipment while meeting a series of constraints such as coverage, bandwidth, maximum delay, and redundancy, all while minimizing network cost. As network size and constraints grow, the design process can quickly become overwhelming. In this thesis we model the network design problem (NDP) as a generalized Steiner tree-star (GSTS) problem. Any solution to the minimum Steiner tree (MST) problem on a constructed GSTS
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Schultze, Martin [Verfasser]. "Constructing Subtests Using Ant Colony Optimization / Martin Schultze." Berlin : Freie Universität Berlin, 2017. http://d-nb.info/1138980714/34.

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Książki na temat "Secure Ant Colony Optimization"

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Dorigo, Marco. Ant colony optimization. MIT Press, 2004.

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Fidanova, Stefka. Ant Colony Optimization and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67380-2.

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Solnon, Christine, and Narendra Jussien, eds. Ant Colony Optimization and Constraint Programming. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.

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Dorigo, Marco, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, and Alan F. T. Winfield, eds. Ant Colony Optimization and Swarm Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87527-7.

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Dorigo, Marco, Luca Maria Gambardella, Mauro Birattari, Alcherio Martinoli, Riccardo Poli, and Thomas Stützle, eds. Ant Colony Optimization and Swarm Intelligence. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088.

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Dorigo, Marco, Mauro Birattari, Christian Blum, Luca Maria Gambardella, Francesco Mondada, and Thomas Stützle, eds. Ant Colony Optimization and Swarm Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b99492.

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Solnon, Christine. Ant colony optimization and constraint programming. Ltd/John Wiley, 2010.

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Solnon, Christine. Ant colony optimization and constraint programming. Ltd/John Wiley, 2010.

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Dey, Nilanjan, ed. Applications of Ant Colony Optimization and its Variants. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7227-2.

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Kozak, Jan. Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-93752-6.

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Części książek na temat "Secure Ant Colony Optimization"

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Azizi, Mounir, Redouane Benaini, and Mouad Ben Mamoun. "Delay-Bandwidth Optimization Method Based on Ant Colony Algorithm Applied to Transport Network Using SDN Paradigm." In Mobile, Secure, and Programmable Networking. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22885-9_3.

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Suresh, Salini, H. R. Bhavani, and M. Hanumanthappa. "A Secured Cloud-Based Framework for Image Processing Using Ant Colony Optimization." In Rising Threats in Expert Applications and Solutions. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6014-9_25.

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Du, Ke-Lin, and M. N. S. Swamy. "Ant Colony Optimization." In Search and Optimization by Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_11.

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Maniezzo, Vittorio, Luca Maria Gambardella, and Fabio de Luigi. "Ant Colony Optimization." In New Optimization Techniques in Engineering. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39930-8_5.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, et al. "Ant Colony Optimization." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_22.

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Mahapatra, Bandana, and Srikanta Patnaik. "Ant Colony Optimization." In Advances in Swarm Intelligence for Optimizing Problems in Computer Science. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429445927-4.

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Becceneri, José Carlos, Stephan Stephany, Haroldo Fraga de Campos Velho, and Antônio José da Silva Neto. "Ant Colony Optimization." In Computational Intelligence Applied to Inverse Problems in Radiative Transfer. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43544-7_8.

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Stützle, Thomas. "Ant Colony Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01020-0_2.

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Fidanova, Stefka. "Ant Colony Optimization." In Ant Colony Optimization and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67380-2_2.

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Dorigo, Marco, and Mauro Birattari. "Ant Colony Optimization." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_22.

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Streszczenia konferencji na temat "Secure Ant Colony Optimization"

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Xiao, Dongsheng. "A Secure Encryption Method for Computer Network Communication Data based on Improved Ant Colony Optimization Algorithm." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859968.

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Wang, Biao, Dan-Ting Duan, Qiang Yang, et al. "Adaptive Ant Selection for Pheromone Update in Ant Colony Optimization." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831815.

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Bhetiwal, Surikha, and Santanu Kr Misra. "Survey on Task Scheduling with Ant Colony Optimization." In 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, 2023. http://dx.doi.org/10.1109/icsccc58608.2023.10176927.

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Sun, Ruoying, Xingfen Wang, and Gang Zhao. "An Ant Colony Optimization Approach to Multi-Objective Supply Chain Model." In 2008 Second IEEE International Conference on Secure System Integration and Reliability Improvement (SSIRI). IEEE, 2008. http://dx.doi.org/10.1109/ssiri.2008.35.

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Hasija, Hitesh, and Rahul Katarya. "Secure code assignment to alphabets using modified ant colony optimization along with compression." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968499.

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Sharmin, Afsah, F. Anwar, S. M. A. Motakabber, and Aisha-Hassan A. Hashim. "A Trust Aware Secure Ant Colony Optimization Based Routing Algorithm for Internet of Things." In 2023 9th International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2023. http://dx.doi.org/10.1109/iccce58854.2023.10246057.

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Dai, Yuanyuan, Qi Chen, and Jian Nie. "Optimization Of Secure Propagation Path Of New Media Short Video Based On Ant Colony Algorithm." In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9941796.

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V, Vishnuvardhan, Geethanjali A, Ramesh SB, Vamsi M, Jeevitha M, and Aravind SM. "Enhancing Secure Communication in VANETs with Blockchain and Privacy using PSOACO Algorithm." In Sri Venkatesa Perumal College of Engineering and Technology. International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/hlww7128/svpcet24p19.

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This study explores the optimization of shortest paths in intelligent transportation systems using a hybrid Particle Swarm Optimization and Ant Colony Optimization (PSOACO) algorithm. The study focuses on improving traffic flow efficiency and minimizing travel time by finding the most optimal routes for vehicles. Utilizing simulations and algorithmic analysis, the PSOACO algorithm is evaluated for its effectiveness in guiding vehicles through complex traffic networks. The results demonstrate significant improvements in route planning and traffic coordination, showcasing the potential of hybrid
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Karuppaiah, Jayakumar, C. Aarthi, Girirajan, M. Jananai, and Parameshachari B. D. "Secure Smart Healthcare Surveillance Framework Using Fuzzy C-Means Clustering with Effective Ant Colony Optimization in Internet of Things." In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2022. http://dx.doi.org/10.1109/ickecs56523.2022.10060099.

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Wang, Yangping, Chengwei Tang, Jingyu Yang, and Linjing Wei. "Road Extraction from High-Resolution Remotely Sensed Image Based on Improved Ant Colony Optimization Method." In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018. http://dx.doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.00058.

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Raporty organizacyjne na temat "Secure Ant Colony Optimization"

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Marugan, Singaravelan, and Bhuvaneswari Mariappan. Self Learning Ant Colony Optimization in Multipath Routing Ad Hoc Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.10.12.

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